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null | https://openreview.net/forum?id=As91fJvY9E | @inproceedings{
liu2024endtoend,
title={End-to-end Learnable Clustering for Intent Learning in Recommendation},
author={Yue Liu and Shihao Zhu and Jun Xia and YINGWEI MA and Jian Ma and Xinwang Liu and Shengju Yu and Kejun Zhang and Wenliang Zhong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=As91fJvY9E}
} | Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, existing methods suffer from complex and cumbersome alternating optimization, limiting performance and scalability. To this end, we propose a novel intent learning method termed \underline{ELCRec}, by unifying behavior representation learning into an \underline{E}nd-to-end \underline{L}earnable \underline{C}lustering framework, for effective and efficient \underline{Rec}ommendation. Concretely, we encode user behavior sequences and initialize the cluster centers (latent intents) as learnable neurons. Then, we design a novel learnable clustering module to separate different cluster centers, thus decoupling users' complex intents. Meanwhile, it guides the network to learn intents from behaviors by forcing behavior embeddings close to cluster centers. This allows simultaneous optimization of recommendation and clustering via mini-batch data. Moreover, we propose intent-assisted contrastive learning by using cluster centers as self-supervision signals, further enhancing mutual promotion. Both experimental results and theoretical analyses demonstrate the superiority of ELCRec from six perspectives. Compared to the runner-up, ELCRec improves NDCG@5 by 8.9\% and reduces computational costs by 22.5\% on the Beauty dataset. Furthermore, due to the scalability and universal applicability, we deploy this method on the industrial recommendation system with 130 million page views and achieve promising results. The codes are available on GitHub\footnote{https://github.com/yueliu1999/ELCRec}. A collection (papers, codes, datasets) of deep group recommendation/intent learning methods is available on GitHub\footnote{https://github.com/yueliu1999/Awesome-Deep-Group-Recommendation}. | End-to-end Learnable Clustering for Intent Learning in Recommendation | [
"Yue Liu",
"Shihao Zhu",
"Jun Xia",
"YINGWEI MA",
"Jian Ma",
"Xinwang Liu",
"Shengju Yu",
"Kejun Zhang",
"Wenliang Zhong"
] | NeurIPS.cc/2024/Conference | 2401.05975 | [
"https://github.com/yueliu1999/elcrec"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AqcPvWwktK | @inproceedings{
li2024semisupervised,
title={Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss},
author={Ximing Li and Silong Liang and Changchun Li and pengfei wang and Fangming Gu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AqcPvWwktK}
} | Semi-supervised multi-label learning (SSMLL) refers to inducing classifiers using a small number of samples with multiple labels and many unlabeled samples. The prevalent solution of SSMLL involves forming pseudo-labels for unlabeled samples and inducing classifiers using both labeled and pseudo-labeled samples in a self-training manner. Unfortunately, with the commonly used binary type of loss and negative sampling, we have empirically found that learning with labeled and pseudo-labeled samples can result in the variance bias problem between the feature distributions of positive and negative samples for each label. To alleviate this problem, we aim to balance the variance bias between positive and negative samples from the perspective of the feature angle distribution for each label. Specifically, we extend the traditional binary angular margin loss to a balanced extension with feature angle distribution transformations under the Gaussian assumption, where the distributions are iteratively updated during classifier training. We also suggest an efficient prototype-based negative sampling method to maintain high-quality negative samples for each label. With this insight, we propose a novel SSMLL method, namely Semi-Supervised Multi-Label Learning with Balanced Binary Angular Margin loss (S$^2$ML$^2$-BBAM). To evaluate the effectiveness of S$^2$ML$^2$-BBAM, we compare it with existing competitors on benchmark datasets. The experimental results validate that S$^2$ML$^2$-BBAM can achieve very competitive performance. | Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss | [
"Ximing Li",
"Silong Liang",
"Changchun Li",
"pengfei wang",
"Fangming Gu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=AprsVxrwXT | @inproceedings{
yi2024mvgamba,
title={{MVG}amba: Unify 3D Content Generation as State Space Sequence Modeling},
author={Xuanyu Yi and Zike Wu and Qiuhong Shen and Qingshan Xu and Pan Zhou and Joo Hwee Lim and Shuicheng YAN and Xinchao Wang and Hanwang Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AprsVxrwXT}
} | Recent 3D large reconstruction models (LRMs) can generate high-quality 3D content in sub-seconds by integrating multi-view diffusion models with scalable multi-view reconstructors. Current works further leverage 3D Gaussian Splatting as 3D representation for improved visual quality and rendering efficiency. However, we observe that existing Gaussian reconstruction models often suffer from multi-view inconsistency and blurred textures. We attribute this to the compromise of multi-view information propagation in favor of adopting powerful yet computationally intensive architectures (\eg, Transformers).
To address this issue, we introduce MVGamba, a general and lightweight Gaussian reconstruction model featuring a multi-view Gaussian reconstructor based on the RNN-like State Space Model (SSM). Our Gaussian reconstructor propagates causal context containing multi-view information for cross-view self-refinement while generating a long sequence of Gaussians for fine-detail modeling with linear complexity.
With off-the-shelf multi-view diffusion models integrated, MVGamba unifies 3D generation tasks from a single image, sparse images, or text prompts. Extensive experiments demonstrate that MVGamba outperforms state-of-the-art baselines in all 3D content generation scenarios with approximately only $0.1\times$ of the model size. The codes are available at \url{https://github.com/SkyworkAI/MVGamba}. | MVGamba: Unify 3D Content Generation as State Space Sequence Modeling | [
"Xuanyu Yi",
"Zike Wu",
"Qiuhong Shen",
"Qingshan Xu",
"Pan Zhou",
"Joo Hwee Lim",
"Shuicheng YAN",
"Xinchao Wang",
"Hanwang Zhang"
] | NeurIPS.cc/2024/Conference | 2406.06367 | [
""
] | https://huggingface.co/papers/2406.06367 | 0 | 0 | 0 | 9 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=Apq6corvfZ | @inproceedings{
feldman2024instanceoptimal,
title={Instance-Optimal Private Density Estimation in the Wasserstein Distance},
author={Vitaly Feldman and Audra McMillan and Satchit Sivakumar and Kunal Talwar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Apq6corvfZ}
} | Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances.
For distributions $P$ over $\mathbb{R}$, we consider a strong notion of instance-optimality: an algorithm that uniformly achieves the instance-optimal estimation rate is competitive with an algorithm that is told that the distribution is either $P$ or $Q_P$ for some distribution $Q_P$ whose probability density function (pdf) is within a factor of 2 of the pdf of $P$. For distributions over $\mathbb{R}^2$, we use a slightly different notion of instance optimality. We say that an algorithm is instance-optimal if it is competitive with an algorithm that is given a constant multiplicative approximation of the density of the distribution. We characterize the instance-optimal estimation rates in both these settings and show that they are uniformly achievable (up to polylogarithmic factors). Our approach for $\mathbb{R}^2$ extends to arbitrary metric spaces as it goes via hierarchically separated trees. As a special case our results lead to instance-optimal learning in TV distance for discrete distributions. | Instance-Optimal Private Density Estimation in the Wasserstein Distance | [
"Vitaly Feldman",
"Audra McMillan",
"Satchit Sivakumar",
"Kunal Talwar"
] | NeurIPS.cc/2024/Conference | 2406.19566 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AoEeBqP8AD | @inproceedings{
xiao2024unsupervised,
title={Unsupervised Anomaly Detection in The Presence of Missing Values},
author={Feng Xiao and Jicong Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AoEeBqP8AD}
} | Anomaly detection methods typically require fully observed data for model training and inference and cannot handle incomplete data, while the missing data problem is pervasive in science and engineering, leading to challenges in many important applications such as abnormal user detection in recommendation systems and novel or anomalous cell detection in bioinformatics, where the missing rates can be higher than 30\% or even 80\%. In this work, first, we construct and evaluate a straightforward strategy, ''impute-then-detect'', via combining state-of-the-art imputation methods with unsupervised anomaly detection methods, where the training data are composed of normal samples only. We observe that such two-stage methods frequently yield imputation bias from normal data, namely, the imputation methods are inclined to make incomplete samples ''normal", where the fundamental reason is that the imputation models learned only on normal data and cannot generalize well to abnormal data in the inference stage. To address this challenge, we propose an end-to-end method that integrates data imputation with anomaly detection into a unified optimization problem. The proposed model learns to generate well-designed pseudo-abnormal samples to mitigate the imputation bias and ensure the discrimination ability of both the imputation and detection processes. Furthermore, we provide theoretical guarantees for the effectiveness of the proposed method, proving that the proposed method can correctly detect anomalies with high probability. Experimental results on datasets with manually constructed missing values and inherent missing values demonstrate that our proposed method effectively mitigates the imputation bias and surpasses the baseline methods significantly. The source code of our method is available at https://github.com/jicongfan/ImAD-Anomaly-Detection-With-Missing-Data. | Unsupervised Anomaly Detection in The Presence of Missing Values | [
"Feng Xiao",
"Jicong Fan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Ao0FiZqrXa | @inproceedings{
zhou2024simple,
title={Simple and Fast Distillation of Diffusion Models},
author={Zhenyu Zhou and Defang Chen and Can Wang and Chun Chen and Siwei Lyu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ao0FiZqrXa}
} | Diffusion-based generative models have demonstrated their powerful performance across various tasks, but this comes at a cost of the slow sampling speed. To achieve both efficient and high-quality synthesis, various distillation-based accelerated sampling methods have been developed recently. However, they generally require time-consuming fine tuning with elaborate designs to achieve satisfactory performance in a specific number of function evaluation (NFE), making them difficult to employ in practice. To address this issue, we propose **S**imple and **F**ast **D**istillation (SFD) of diffusion models, which simplifies the paradigm used in existing methods and largely shortens their fine-tuning time up to $1000\times$. We begin with a vanilla distillation-based sampling method and boost its performance to state of the art by identifying and addressing several small yet vital factors affecting the synthesis efficiency and quality. Our method can also achieve sampling with variable NFEs using a single distilled model. Extensive experiments demonstrate that SFD strikes a good balance between the sample quality and fine-tuning costs in few-step image generation task. For example, SFD achieves 4.53 FID (NFE=2) on CIFAR-10 with only **0.64 hours** of fine-tuning on a single NVIDIA A100 GPU. | Simple and Fast Distillation of Diffusion Models | [
"Zhenyu Zhou",
"Defang Chen",
"Can Wang",
"Chun Chen",
"Siwei Lyu"
] | NeurIPS.cc/2024/Conference | 2409.19681 | [
"https://github.com/zju-pi/diff-sampler"
] | https://huggingface.co/papers/2409.19681 | 0 | 0 | 0 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=AkiPax5SXu | @inproceedings{
ito2024on,
title={On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice},
author={Shinji Ito},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AkiPax5SXu}
} | This paper examines two extensions of multi-armed bandit problems: multi-armed bandits with expert advice and contextual linear bandits. For the former problem, multi-armed bandits with expert advice, the previously known best upper and lower bounds have been $O(\sqrt{KT \log \frac{N}{K} })$ and $\Omega( \sqrt{KT \frac{ \log N }{\log K }} )$, respectively. Here, $K$, $N$, and $T$ represent the numbers of arms, experts, and rounds, respectively. This paper closes the gap between these bounds by presenting a matching lower bound of $\Omega( \sqrt{KT \log \frac{N}{K}} )$.
This lower bound is shown for the problem setting in which the player chooses an expert before observing the advices in each round.
For the latter problem, contextual linear bandits, we provide an algorithm that achieves $O ( \sqrt{d T \log ( K \min\{ 1, \frac{S}{d} \} )} )$ together with a matching lower bound, where $d$ and $S$ represent the dimensionality of feature vectors and the size of the context space, respectively. | On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice | [
"Shinji Ito"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Aj8RKCGwjE | @inproceedings{
serrano2024aroma,
title={{AROMA}: Preserving Spatial Structure for Latent {PDE} Modeling with Local Neural Fields},
author={Louis Serrano and Thomas X Wang and Etienne Le Naour and Jean-No{\"e}l Vittaut and Patrick Gallinari},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Aj8RKCGwjE}
} | We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent representations of spatial physical fields from a variety of data types, including irregular-grid inputs and point clouds. This versatility eliminates the need for patching and allows efficient processing of diverse geometries. The sequential nature of our latent representation can be interpreted spatially and permits the use of a conditional transformer for modeling the temporal dynamics of PDEs. By employing a diffusion-based formulation, we achieve greater stability and enable longer rollouts compared to conventional MSE training. AROMA's superior performance in simulating 1D and 2D equations underscores the efficacy of our approach in capturing complex dynamical behaviors. | AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields | [
"Louis Serrano",
"Thomas X Wang",
"Etienne Le Naour",
"Jean-Noël Vittaut",
"Patrick Gallinari"
] | NeurIPS.cc/2024/Conference | 2406.02176 | [
"https://github.com/louisserrano/aroma"
] | https://huggingface.co/papers/2406.02176 | 1 | 0 | 0 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=Aj0Zf28l6o | @inproceedings{
park2024equivariant,
title={Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation},
author={Jiwoong Park and Yang Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Aj0Zf28l6o}
} | How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world?
In this paper, we address the question by focusing on a fundamental biochemical problem of generating 3D molecular conformers conditioned on molecular graphs in a multiscale manner.
Our approach consists of two hierarchical stages: i) generation of coarse-grained fragment-level 3D structure from the molecular graph, and ii) generation of fine atomic details from the coarse-grained approximated structure while allowing the latter to be adjusted simultaneously.
For the challenging second stage, which demands preserving coarse-grained information while ensuring SE(3) equivariance, we introduce a novel generative model termed Equivariant Blurring Diffusion (EBD), which defines a forward process that moves towards the fragment-level coarse-grained structure by blurring the fine atomic details of conformers, and a reverse process that performs the opposite operation using equivariant networks.
We demonstrate the effectiveness of EBD by geometric and chemical comparison to state-of-the-art denoising diffusion models on a benchmark of drug-like molecules.
Ablation studies draw insights on the design of EBD by thoroughly analyzing its architecture, which includes the design of the loss function and the data corruption process.
Codes are released at https://github.com/Shen-Lab/EBD. | Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation | [
"Jiwoong Park",
"Yang Shen"
] | NeurIPS.cc/2024/Conference | 2410.20255 | [
"https://github.com/shen-lab/ebd"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AiMs8GPP5q | @inproceedings{
yerxa2024contrastiveequivariant,
title={Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area {IT}},
author={Thomas Edward Yerxa and Jenelle Feather and Eero P Simoncelli and SueYeon Chung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AiMs8GPP5q}
} | Models trained with self-supervised learning objectives have recently matched or surpassed models trained with traditional supervised object recognition in their ability to predict neural responses of object-selective neurons in the primate visual system. A self-supervised learning objective is arguably a more biologically plausible organizing principle, as the optimization does not require a large number of labeled examples. However, typical self-supervised objectives may result in network representations that are overly invariant to changes in the input. Here, we show that a representation with structured variability to the input transformations is better aligned with known features of visual perception and neural computation. We introduce a novel framework for converting standard invariant SSL losses into "contrastive-equivariant" versions that encourage preserving aspects of the input transformation without supervised access to the transformation parameters. We further demonstrate that our proposed method systematically increases models' ability to predict responses in macaque inferior temporal cortex. Our results demonstrate the promise of incorporating known features of neural computation into task-optimization for building better models of visual cortex. | Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT | [
"Thomas Edward Yerxa",
"Jenelle Feather",
"Eero P Simoncelli",
"SueYeon Chung"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Ai76ATrb2y | @inproceedings{
busa-fekete2024auditing,
title={Auditing Privacy Mechanisms via Label Inference Attacks},
author={Robert Istvan Busa-Fekete and Travis Dick and Claudio Gentile and Andres Munoz medina and Adam Smith and Marika Swanberg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ai76ATrb2y}
} | We propose reconstruction advantage measures to audit label privatization mechanisms. A reconstruction advantage measure quantifies the increase in an attacker's ability to infer the true label of an unlabeled example when provided with a private version of the labels in a dataset (e.g., aggregate of labels from different users or noisy labels output by randomized response), compared to an attacker that only observes the feature vectors, but may have prior knowledge of the correlation between features and labels. We consider two such auditing measures: one additive, and on multiplicative. These cover previous approaches taken in the literature on empirical auditing and differential privacy. These measures allow us to place a variety of proposed privatization schemes---some differentially private, some not---on the same footing. We analyze these measures theoretically under a distributional model which, we claim, encapsulates reasonable adversarial settings. We also quantify their behavior empirically on real and simulated prediction tasks. Across a range of experimental settings, we find that differentially private schemes dominate or match the privacy-utility tradeoff of more heuristic approaches. | Auditing Privacy Mechanisms via Label Inference Attacks | [
"Robert Istvan Busa-Fekete",
"Travis Dick",
"Claudio Gentile",
"Andres Munoz medina",
"Adam Smith",
"Marika Swanberg"
] | NeurIPS.cc/2024/Conference | 2406.02797 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=AhlaBDHMQh | @inproceedings{
mao2024learning,
title={Learning Identifiable Factorized Causal Representations of Cellular Responses},
author={Haiyi Mao and Romain Lopez and Kai Liu and Jan-Christian Huetter and David Richmond and Panayiotis V. Benos and Lin Qiu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AhlaBDHMQh}
} | The study of cells and their responses to genetic or chemical perturbations promises to accelerate the discovery of therapeutics targets. However, designing adequate and insightful models for such data is difficult because the response of a cell to perturbations essentially depends on contextual covariates (e.g., genetic background or type of the cell). There is therefore a need for models that can identify interactions between drugs and contextual covariates. This is crucial for discovering therapeutics targets, as such interactions may reveal drugs that affect certain cell types but not others.
We tackle this problem with a novel Factorized Causal Representation (FCR) learning method, an identifiable deep generative model that reveals causal structure in single-cell perturbation data from several cell lines. FCR learns multiple cellular representations that are disentangled, comprised of covariate-specific (Z_x), treatment-specific (Z_t) and interaction-specific (Z_tx) representations. Based on recent advances of non-linear ICA theory, we prove the component-wise identifiability of Z_tx and block-wise identifiability of Z_t and Z_x. Then, we present our implementation of FCR, and empirically demonstrate that FCR outperforms state-of-the-art baselines in various tasks across four single-cell datasets. | Learning Identifiable Factorized Causal Representations of Cellular Responses | [
"Haiyi Mao",
"Romain Lopez",
"Kai Liu",
"Jan-Christian Huetter",
"David Richmond",
"Panayiotis V. Benos",
"Lin Qiu"
] | NeurIPS.cc/2024/Conference | 2410.22472 | [
"https://github.com/Genentech/fcr"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AhjTu2aiiW | @inproceedings{
norman2024firstexplore,
title={First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs},
author={Ben Norman and Jeff Clune},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AhjTu2aiiW}
} | Standard reinforcement learning (RL) agents never intelligently explore like a human (i.e. taking into account complex domain priors and adapting quickly based on previous exploration). Across episodes, RL agents struggle to perform even simple exploration strategies, for example systematic search that avoids exploring the same location multiple times. This poor exploration limits performance on challenging domains. Meta-RL is a potential solution, as unlike standard RL, meta-RL can *learn* to explore, and potentially learn highly complex strategies far beyond those of standard RL, strategies such as experimenting in early episodes to learn new skills, or conducting experiments to learn about the current environment.
Traditional meta-RL focuses on the problem of learning to optimally balance exploration and exploitation to maximize the *cumulative reward* of the episode sequence (e.g., aiming to maximize the total wins in a tournament -- while also improving as a player).
We identify a new challenge with state-of-the-art cumulative-reward meta-RL methods.
When optimal behavior requires exploration that sacrifices immediate reward to enable higher subsequent reward, existing state-of-the-art cumulative-reward meta-RL methods become stuck on the local optimum of failing to explore.
Our method, First-Explore, overcomes this limitation by learning two policies: one to solely explore, and one to solely exploit. When exploring requires forgoing early-episode reward, First-Explore significantly outperforms existing cumulative meta-RL methods. By identifying and solving the previously unrecognized problem of forgoing reward in early episodes, First-Explore represents a significant step towards developing meta-RL algorithms capable of human-like exploration on a broader range of domains. | First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs | [
"Ben Norman",
"Jeff Clune"
] | NeurIPS.cc/2024/Conference | 2307.02276 | [
"https://github.com/btnorman/First-Explore"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AfzbDw6DSp | @inproceedings{
sanford2024understanding,
title={Understanding Transformer Reasoning Capabilities via Graph Algorithms},
author={Clayton Sanford and Bahare Fatemi and Ethan Hall and Anton Tsitsulin and Mehran Kazemi and Jonathan Halcrow and Bryan Perozzi and Vahab Mirrokni},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AfzbDw6DSp}
} | Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based neural networks, a theoretical understanding of their algorithmic reasoning capabilities in realistic parameter regimes is lacking. We investigate this question in terms of the network’s depth, width, and number of extra tokens for algorithm execution. Our novel representational hierarchy separates 9 algorithmic reasoning problems into classes solvable by transformers in different realistic parameter scaling regimes. We prove that logarithmic depth is necessary and sufficient for tasks like graph connectivity, while single-layer transformers with small embedding dimensions can solve contextual retrieval tasks. We also support our theoretical analysis with ample empirical evidence using the GraphQA benchmark. These results show that transformers excel at many graph reasoning tasks, even outperforming specialized graph neural networks. | Understanding Transformer Reasoning Capabilities via Graph Algorithms | [
"Clayton Sanford",
"Bahare Fatemi",
"Ethan Hall",
"Anton Tsitsulin",
"Mehran Kazemi",
"Jonathan Halcrow",
"Bryan Perozzi",
"Vahab Mirrokni"
] | NeurIPS.cc/2024/Conference | 2405.18512 | [
""
] | https://huggingface.co/papers/2405.18512 | 2 | 1 | 0 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=AdS3H8SaPi | @inproceedings{
chidambaram2024what,
title={What does guidance do? A fine-grained analysis in a simple setting},
author={Muthu Chidambaram and Khashayar Gatmiry and Sitan Chen and Holden Lee and Jianfeng Lu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AdS3H8SaPi}
} | The use of guidance in diffusion models was originally motivated by the premise that the guidance-modified score is that of the data distribution tilted by a conditional likelihood raised to some power. In this work we clarify this misconception by rigorously proving that guidance fails to sample from the intended tilted distribution. Our main result is to give a fine-grained characterization of the dynamics of guidance in two cases, (1) mixtures of compactly supported distributions and (2) mixtures of Gaussians, which reflect salient properties of guidance that manifest on real-world data. In both cases, we prove that as the guidance parameter increases, the guided model samples more heavily from the boundary of the support of the conditional distribution. We also prove that for any nonzero level of score estimation error, sufficiently large guidance will result in sampling away from the support, theoretically justifying the empirical finding that large guidance results in distorted generations. In addition to verifying these results empirically in synthetic settings, we also show how our theoretical insights can offer useful prescriptions for practical deployment. | What does guidance do? A fine-grained analysis in a simple setting | [
"Muthu Chidambaram",
"Khashayar Gatmiry",
"Sitan Chen",
"Holden Lee",
"Jianfeng Lu"
] | NeurIPS.cc/2024/Conference | 2409.13074 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Ad3PzTuqIq | @inproceedings{
cherenkova2024spelsnet,
title={SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision},
author={Kseniya Cherenkova and Elona Dupont and Anis Kacem and Gleb A Gusev and Djamila Aouada},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ad3PzTuqIq}
} | Within the realm of Computer-Aided Design (CAD), Boundary-Representation (B-Rep) is the standard option for modeling shapes. We present SpelsNet, a neural architecture for the segmentation of 3D point clouds into surface primitive elements under topological supervision of its B-Rep graph structure. We also propose a point-to-BRep adjacency representation that allows for adapting conventional Linear Algebraic Representation of B-Rep graph structure to the point cloud domain. Thanks to this representation, SpelsNet learns from both spatial and topological domains to enable accurate and topologically consistent surface primitive element segmentation. In particular, SpelsNet is composed of two main components; (1) a supervised 3D spatial segmentation head that outputs B-Rep element types and memberships; (2) a graph-based head that leverages the proposed topological supervision. To enable the learning of SpelsNet with the proposed point-to-BRep adjacency supervision, we extend two existing CAD datasets with the required annotations, and conduct a thorough experimental validation on them. The obtained results showcase the efficacy of SpelsNet and its topological supervision compared to a set of baselines and state-of-the-art approaches. | SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision | [
"Kseniya Cherenkova",
"Elona Dupont",
"Anis Kacem",
"Gleb A Gusev",
"Djamila Aouada"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Actjv6Wect | @inproceedings{
caragiannis2024proportional,
title={Proportional Fairness in Non-Centroid Clustering},
author={Ioannis Caragiannis and Evi Micha and Nisarg Shah},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Actjv6Wect}
} | We revisit the recently developed framework of proportionally fair clustering, where the goal is to provide group fairness guarantees that become stronger for groups of data points that are large and cohesive. Prior work applies this framework to centroid-based clustering, where points are partitioned into clusters, and the cost to each data point is measured by its distance to a centroid assigned to its cluster. However, real-life applications often do not require such centroids. We extend the theory of proportionally fair clustering to non-centroid clustering by considering a variety of cost functions, both metric and non-metric, for a data point to be placed in a cluster with other data points. Our results indicate that Greedy Capture, a clustering algorithm developed for centroid clustering, continues to provide strong proportional fairness guarantees for non-centroid clustering, although the guarantees are significantly different and establishing them requires novel proof ideas. We also design algorithms for auditing proportional fairness of a given clustering solution. We conduct experiments on real data which suggest that traditional clustering algorithms are highly unfair, while our algorithms achieve strong fairness guarantees with a moderate loss in common clustering objectives. | Proportional Fairness in Non-Centroid Clustering | [
"Ioannis Caragiannis",
"Evi Micha",
"Nisarg Shah"
] | NeurIPS.cc/2024/Conference | 2410.23273 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AcBLtTKK5q | @inproceedings{
jin2024jailbreaking,
title={Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters},
author={Haibo Jin and Andy Zhou and Joe D. Menke and Haohan Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AcBLtTKK5q}
} | Large Language Models (LLMs) are typically harmless but remain vulnerable to carefully crafted prompts known as ``jailbreaks'', which can bypass protective measures and induce harmful behavior. Recent advancements in LLMs have incorporated moderation guardrails that can filter outputs, which trigger processing errors for certain malicious questions. Existing red-teaming benchmarks often neglect to include questions that trigger moderation guardrails, making it difficult to evaluate jailbreak effectiveness. To address this issue, we introduce JAMBench, a harmful behavior benchmark designed to trigger and evaluate moderation guardrails. JAMBench involves 160 manually crafted instructions covering four major risk categories at multiple severity levels. Furthermore, we propose a jailbreak method, JAM (Jailbreak Against Moderation), designed to attack moderation guardrails using jailbreak prefixes to bypass input-level filters and a fine-tuned shadow model functionally equivalent to the guardrail model to generate cipher characters to bypass output-level filters. Our extensive experiments on four LLMs demonstrate that JAM achieves higher jailbreak success ($\sim$ $\times$ 19.88) and lower filtered-out rates ($\sim$ $\times$ 1/6) than baselines. | Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters | [
"Haibo Jin",
"Andy Zhou",
"Joe D. Menke",
"Haohan Wang"
] | NeurIPS.cc/2024/Conference | 2405.20413 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AbZyNGWfpN | @inproceedings{
shen2024expanding,
title={Expanding Sparse Tuning for Low Memory Usage},
author={Shufan Shen and Junshu Sun and Xiangyang Ji and Qingming Huang and Shuhui Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AbZyNGWfpN}
} | Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters. Among PEFT methods, sparse tuning achieves superior performance by only adjusting the weights most relevant to downstream tasks, rather than densely tuning the whole weight matrix. However, this performance improvement has been accompanied by increases in memory usage, which stems from two factors, i.e., the storage of the whole weight matrix as learnable parameters in the optimizer and the additional storage of tunable weight indexes. In this paper, we propose a method named SNELL (Sparse tuning with kerNELized LoRA) for sparse tuning with low memory usage. To achieve low memory usage, SNELL decomposes the tunable matrix for sparsification into two learnable low-rank matrices, saving from the costly storage of the whole original matrix. A competition-based sparsification mechanism is further proposed to avoid the storage of tunable weight indexes. To maintain the effectiveness of sparse tuning with low-rank matrices, we extend the low-rank decomposition by applying nonlinear kernel functions to the whole-matrix merging. Consequently, we gain an increase in the rank of the merged matrix, enhancing the ability of SNELL in adapting the pre-trained models to downstream tasks. Extensive experiments on multiple downstream tasks show that SNELL achieves state-of-the-art performance with low memory usage, endowing PEFT with sparse tuning to large-scale models. Codes are available at https://github.com/ssfgunner/SNELL. | Expanding Sparse Tuning for Low Memory Usage | [
"Shufan Shen",
"Junshu Sun",
"Xiangyang Ji",
"Qingming Huang",
"Shuhui Wang"
] | NeurIPS.cc/2024/Conference | 2411.01800 | [
"https://github.com/ssfgunner/snell"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AbTpJl7vN6 | @inproceedings{
sandbrink2024flexible,
title={Flexible task abstractions emerge in linear networks with fast and bounded units},
author={Kai Jappe Sandbrink and Jan Philipp Bauer and Alexandra Maria Proca and Andrew M Saxe and Christopher Summerfield and Ali Hummos},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AbTpJl7vN6}
} | Animals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts. Animals can then respond flexibly by selecting the appropriate task abstraction. However, how such flexible task abstractions may arise in neural systems remains unknown. Here, we analyze a linear gated network where the weights and gates are jointly optimized via gradient descent, but with neuron-like constraints on the gates including a faster timescale, non-negativity, and bounded activity. We observe that the weights self-organize into modules specialized for tasks or sub-tasks encountered, while the gates layer forms unique representations that switch the appropriate weight modules (task abstractions). We analytically reduce the learning dynamics to an effective eigenspace, revealing a virtuous cycle: fast adapting gates drive weight specialization by protecting previous knowledge, while weight specialization in turn increases the update rate of the gating layer. Task switching in the gating layer accelerates as a function of curriculum block size and task training, mirroring key findings in cognitive neuroscience. We show that the discovered task abstractions support generalization through both task and subtask composition, and we extend our findings to a non-linear network switching between two tasks. Overall, our work offers a theory of cognitive flexibility in animals as arising from joint gradient descent on synaptic and neural gating in a neural network architecture. | Flexible task abstractions emerge in linear networks with fast and bounded units | [
"Kai Jappe Sandbrink",
"Jan Philipp Bauer",
"Alexandra Maria Proca",
"Andrew M Saxe",
"Christopher Summerfield",
"Ali Hummos"
] | NeurIPS.cc/2024/Conference | 2411.03840 | [
"https://github.com/aproca/neural_task_abstraction"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=AYq6GxxrrY | @inproceedings{
klein2024transferable,
title={Transferable Boltzmann Generators},
author={Leon Klein and Frank Noe},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AYq6GxxrrY}
} | The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics. Boltzmann Generators are a generative machine learning method that addresses this issue by learning a transformation via a normalizing flow from a simple prior distribution to the target Boltzmann distribution of interest. Recently, flow matching has been employed to train Boltzmann Generators for small molecular systems in Cartesian coordinates. We extend this work and propose a first framework for Boltzmann Generators that are transferable across chemical space, such that they predict zero-shot Boltzmann distributions for test molecules without being retraining for these systems. These transferable Boltzmann Generators allow approximate sampling from the target distribution of unseen systems, as well as efficient reweighting to the target Boltzmann distribution. The transferability of the proposed framework is evaluated on dipeptides, where we show that it generalizes efficiently to unseen systems.
Furthermore, we demonstrate that our proposed architecture enhances the efficiency of Boltzmann Generators trained on single molecular systems. | Transferable Boltzmann Generators | [
"Leon Klein",
"Frank Noe"
] | NeurIPS.cc/2024/Conference | 2406.14426 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AYntCZvoLI | @inproceedings{
han2024causal,
title={Causal Context Adjustment Loss for Learned Image Compression},
author={Minghao Han and Shiyin Jiang and Shengxi Li and Xin Deng and Mai Xu and Ce Zhu and Shuhang Gu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AYntCZvoLI}
} | In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniques are VAE-based with an autoregressive entropy model, which obviously promotes the RD performance by utilizing the decoded causal context. However, extant methods are highly dependent on the fixed hand-crafted causal context. The question of how to guide the auto-encoder to generate a more effective causal context benefit for the autoregressive entropy models is worth exploring. In this paper, we make the first attempt in investigating the way to explicitly adjust the causal context with our proposed Causal Context Adjustment loss (CCA-loss). By imposing the CCA-loss, we enable the neural network to spontaneously adjust important information into the early stage of the autoregressive entropy model. Furthermore, as transformer technology develops remarkably, variants of which have been adopted by many state-of-the-art (SOTA) LIC techniques. The existing computing devices have not adapted the calculation of the attention mechanism well, which leads to a burden on computation quantity and inference latency. To overcome it, we establish a convolutional neural network (CNN) image compression model and adopt the unevenly channel-wise grouped strategy for high efficiency. Ultimately, the proposed CNN-based LIC network trained with our Causal Context Adjustment loss attains a great trade-off between inference latency and rate-distortion performance. | Causal Context Adjustment Loss for Learned Image Compression | [
"Minghao Han",
"Shiyin Jiang",
"Shengxi Li",
"Xin Deng",
"Mai Xu",
"Ce Zhu",
"Shuhang Gu"
] | NeurIPS.cc/2024/Conference | 2410.04847 | [
"https://github.com/LabShuHangGU/CCA"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AYDBFxNon4 | @inproceedings{
ji-an2024linking,
title={Linking In-context Learning in Transformers to Human Episodic Memory},
author={Li Ji-An and Corey Yishan Zhou and Marcus K. Benna and Marcelo G Mattar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AYDBFxNon4}
} | Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence. While many artificial intelligence models have a neuroscience counterpart, such connections are largely missing in Transformer models and the self-attention mechanism. Here, we examine the relationship between interacting attention heads and human episodic memory. We focus on induction heads, which contribute to in-context learning in Transformer-based large language models (LLMs). We demonstrate that induction heads are behaviorally, functionally, and mechanistically similar to the contextual maintenance and retrieval (CMR) model of human episodic memory. Our analyses of LLMs pre-trained on extensive text data show that CMR-like heads often emerge in the intermediate and late layers, qualitatively mirroring human memory biases. The ablation of CMR-like heads suggests their causal role in in-context learning. Our findings uncover a parallel between the computational mechanisms of LLMs and human memory, offering valuable insights into both research fields. | Linking In-context Learning in Transformers to Human Episodic Memory | [
"Li Ji-An",
"Corey Yishan Zhou",
"Marcus K. Benna",
"Marcelo G Mattar"
] | NeurIPS.cc/2024/Conference | 2405.14992 | [
"https://github.com/corxyz/icl-cmr"
] | https://huggingface.co/papers/2405.14992 | 0 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=AXcYtHQnxt | @inproceedings{
truong2024eagle,
title={{EAGLE}: Efficient Adaptive Geometry-based Learning in Cross-view Understanding},
author={Thanh-Dat Truong and Utsav Prabhu and Dongyi Wang and Bhiksha Raj and Susan Gauch and Jeyamkondan Subbiah and Khoa Luu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AXcYtHQnxt}
} | Unsupervised Domain Adaptation has been an efficient approach to transferring the semantic segmentation model across data distributions. Meanwhile, the recent Open-vocabulary Semantic Scene understanding based on large-scale vision language models is effective in open-set settings because it can learn diverse concepts and categories. However, these prior methods fail to generalize across different camera views due to the lack of cross-view geometric modeling. At present, there are limited studies analyzing cross-view learning. To address this problem, we introduce a novel Unsupervised Cross-view Adaptation Learning approach to modeling the geometric structural change across views in Semantic Scene Understanding. First, we introduce a novel Cross-view Geometric Constraint on Unpaired Data to model structural changes in images and segmentation masks across cameras. Second, we present a new Geodesic Flow-based Correlation Metric to efficiently measure the geometric structural changes across camera views. Third, we introduce a novel view-condition prompting mechanism to enhance the view-information modeling of the open-vocabulary segmentation network in cross-view adaptation learning. The experiments on different cross-view adaptation benchmarks have shown the effectiveness of our approach in cross-view modeling, demonstrating that we achieve State-of-the-Art (SOTA) performance compared to prior unsupervised domain adaptation and open-vocabulary semantic segmentation methods. | EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding | [
"Thanh-Dat Truong",
"Utsav Prabhu",
"Dongyi Wang",
"Bhiksha Raj",
"Susan Gauch",
"Jeyamkondan Subbiah",
"Khoa Luu"
] | NeurIPS.cc/2024/Conference | 2406.01429 | [
""
] | https://huggingface.co/papers/2406.01429 | 0 | 0 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=AWFryOJaGi | @inproceedings{
yin2024lambda,
title={Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases},
author={Hang Yin and Liyao Xiang and Dong Ding and Yuheng He and Yihan Wu and Pengzhi Chu and Xinbing Wang and Chenghu Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AWFryOJaGi}
} | We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), yet these entities are unlabeled. The problem arises when the source and target graphs are of different scales, and it is much cheaper to label the matchable pairs than the dangling entities. To address this challenge, we propose the framework \textit{Lambda} for dangling detection and entity alignment. Lambda features a GNN-based encoder called KEESA with a spectral contrastive learning loss for EA and a positive-unlabeled learning algorithm called iPULE for dangling detection. Our dangling detection module offers theoretical guarantees of unbiasedness, uniform deviation bounds, and convergence. Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training. | Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases | [
"Hang Yin",
"Liyao Xiang",
"Dong Ding",
"Yuheng He",
"Yihan Wu",
"Pengzhi Chu",
"Xinbing Wang",
"Chenghu Zhou"
] | NeurIPS.cc/2024/Conference | 2403.10978 | [
"https://github.com/Handon112358/NeurIPS_2024_Learning-Matchable-Prior-For-Entity-Alignment-with-Unlabeled-Dangling-Cases"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AVrGtVrx10 | @inproceedings{
chen2024probabilistic,
title={Probabilistic Conformal Distillation for Enhancing Missing Modality Robustness},
author={mengxi Chen and Fei Zhang and Zihua Zhao and Jiangchao Yao and Ya Zhang and Yanfeng Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AVrGtVrx10}
} | Multimodal models trained on modality-complete data are plagued with severe performance degradation when encountering modality-missing data. Prevalent cross-modal knowledge distillation-based methods precisely align the representation of modality-missing data and that of its modality-complete counterpart to enhance robustness. However, due to the irreparable information asymmetry, this determinate alignment is too stringent, easily inducing modality-missing features to capture spurious factors erroneously. In this paper, a novel multimodal Probabilistic Conformal Distillation (PCD) method is proposed, which considers the inherent indeterminacy in this alignment. Given a modality-missing input, our goal is to learn the unknown Probability Density Function (PDF) of the mapped variables in the modality-complete space, rather than relying on the brute-force point alignment. Specifically, PCD models the modality-missing feature as a probabilistic distribution, enabling it to satisfy two characteristics of the PDF. One is the extremes of probabilities of modality-complete feature points on the PDF, and the other is the geometric consistency between the modeled distributions and the peak points of different PDFs. Extensive experiments on a range of benchmark datasets demonstrate the superiority of PCD over state-of-the-art methods. Code is available at: https://github.com/mxchen-mc/PCD. | Probabilistic Conformal Distillation for Enhancing Missing Modality Robustness | [
"mengxi Chen",
"Fei Zhang",
"Zihua Zhao",
"Jiangchao Yao",
"Ya Zhang",
"Yanfeng Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=AVd7DpiooC | @inproceedings{
zhou2024qkformer,
title={{QKF}ormer: Hierarchical Spiking Transformer using Q-K Attention},
author={Chenlin Zhou and Han Zhang and Zhaokun Zhou and Liutao Yu and Liwei Huang and Xiaopeng Fan and Li Yuan and Zhengyu Ma and Huihui Zhou and Yonghong Tian},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AVd7DpiooC}
} | Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for low energy consumption and high performance. However, there remains a substantial gap in performance between SNNs and Artificial Neural Networks (ANNs). To narrow this gap, we have developed QKFormer, a direct training spiking transformer with the following features: i) _Linear complexity and high energy efficiency_, the novel spike-form Q-K attention module efficiently models the token or channel attention through binary vectors and enables the construction of larger models. ii) _Multi-scale spiking representation_, achieved by a hierarchical structure with the different numbers of tokens across blocks. iii) _Spiking Patch Embedding with Deformed Shortcut (SPEDS)_, enhances spiking information transmission and integration, thus improving overall performance. It is shown that QKFormer achieves significantly superior performance over existing state-of-the-art SNN models on various mainstream datasets. Notably, with comparable size to Spikformer (66.34 M, 74.81\%), QKFormer (64.96 M) achieves a groundbreaking top-1 accuracy of **85.65\%** on ImageNet-1k, substantially outperforming Spikformer by **10.84\%**. To our best knowledge, this is the first time that directly training SNNs have exceeded 85\% accuracy on ImageNet-1K. | QKFormer: Hierarchical Spiking Transformer using Q-K Attention | [
"Chenlin Zhou",
"Han Zhang",
"Zhaokun Zhou",
"Liutao Yu",
"Liwei Huang",
"Xiaopeng Fan",
"Li Yuan",
"Zhengyu Ma",
"Huihui Zhou",
"Yonghong Tian"
] | NeurIPS.cc/2024/Conference | 2403.16552 | [
"https://github.com/zhouchenlin2096/qkformer"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=AUg9D2VjcF | @inproceedings{
li2024one,
title={One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently},
author={Weida Li and Yaoliang Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AUg9D2VjcF}
} | The concept of probabilistic values, such as Beta Shapley values and weighted Banzhaf values, has gained recent attention in applications like feature attribution and data valuation. However, exact computation of these values is often exponentially expensive, necessitating approximation techniques. Prior research has shown that the choice of probabilistic values significantly impacts downstream performance, with no universally superior option. Consequently, one may have to approximate multiple candidates and select the best-performing one. Although there have been many efforts to develop efficient estimators, none are intended to approximate all probabilistic values both simultaneously and efficiently. In this work, we embark on the first exploration of achieving this goal. Adhering to the principle of maximum sample reuse and avoiding amplifying factors, we propose a one-sample-fits-all framework parameterized by a sampling vector to approximate intermediate terms that can be converted to any probabilistic value. Leveraging the concept of $ (\epsilon, \delta) $-approximation, we theoretically identify a key formula that effectively determines the convergence rate of our framework. By optimizing the sampling vector using this formula, we obtain i) a one-for-all estimator that achieves the currently best time complexity for all probabilistic values on average, and ii) a faster generic estimator with the sampling vector optimally tuned for each probabilistic value. Particularly, our one-for-all estimator achieves the fastest convergence rate on Beta Shapley values, including the well-known Shapley value, both theoretically and empirically. Finally, we establish a connection between probabilistic values and the least square regression used in (regularized) datamodels, showing that our one-for-all estimator can solve a family of datamodels simultaneously. Our code is available at https://github.com/watml/one-for-all. | One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently | [
"Weida Li",
"Yaoliang Yu"
] | NeurIPS.cc/2024/Conference | 2410.23808 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AUeTkSymOq | @inproceedings{
tyurin2024freya,
title={Freya {PAGE}: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations},
author={Alexander Tyurin and Kaja Gruntkowska and Peter Richt{\'a}rik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AUeTkSymOq}
} | In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times. We consider smooth nonconvex finite-sum (empirical risk minimization) problems in this setup and introduce a new parallel method, Freya PAGE, designed to handle arbitrarily heterogeneous and asynchronous computations. By being robust to "stragglers" and adaptively ignoring slow computations, Freya PAGE offers significantly improved time complexity guarantees compared to all previous methods, including Asynchronous SGD, Rennala SGD, SPIDER, and PAGE, while requiring weaker assumptions. The algorithm relies on novel generic stochastic gradient collection strategies with theoretical guarantees that can be of interest on their own, and may be used in the design of future optimization methods. Furthermore, we establish a lower bound for smooth nonconvex finite-sum problems in the asynchronous setup, providing a fundamental time complexity limit. This lower bound is tight and demonstrates the optimality of Freya PAGE in the large-scale regime, i.e., when $\sqrt{m} \geq n,$ where $n$ is \# of workers, and $m$ is \# of data samples. | Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations | [
"Alexander Tyurin",
"Kaja Gruntkowska",
"Peter Richtárik"
] | NeurIPS.cc/2024/Conference | 2405.15545 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ATSPPGEmAA | @inproceedings{
jin2024optimal,
title={Optimal Batched Best Arm Identification},
author={Tianyuan Jin and Yu Yang and Jing Tang and Xiaokui Xiao and Pan Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ATSPPGEmAA}
} | We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we aim to find the best arm with probability $1-\delta$ for some small constant $\delta>0$ while minimizing both the sample complexity (total number of arm pulls) and the batch complexity (total number of batches). We propose the three-batch best arm identification (Tri-BBAI) algorithm, which is the first batched algorithm that achieves the optimal sample complexity in the asymptotic setting (i.e., $\delta\rightarrow 0$) and runs in $3$ batches in expectation. Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i.e., $1/\delta$ is finite), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero. Moreover, in the non-asymptotic setting, the complexity of previous batch algorithms is usually conditioned on the event that the best arm is returned (with a probability of at least $1-\delta$), which is potentially unbounded in cases where a sub-optimal arm is returned. In contrast, the complexity of Opt-BBAI does not rely on such an event. This is achieved through a novel procedure that we design for checking whether the best arm is eliminated, which is of independent interest. | Optimal Batched Best Arm Identification | [
"Tianyuan Jin",
"Yu Yang",
"Jing Tang",
"Xiaokui Xiao",
"Pan Xu"
] | NeurIPS.cc/2024/Conference | 2310.14129 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ASv9lQcHCc | @inproceedings{
he2024gomatching,
title={GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching},
author={Haibin He and Maoyuan Ye and Jing Zhang and Juhua Liu and Bo Du and Dacheng Tao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ASv9lQcHCc}
} | Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool.
Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. The code will be released. | GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching | [
"Haibin He",
"Maoyuan Ye",
"Jing Zhang",
"Juhua Liu",
"Bo Du",
"Dacheng Tao"
] | NeurIPS.cc/2024/Conference | 2401.07080 | [
"https://github.com/hxyz-123/gomatching"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ASqdVeifn7 | @inproceedings{
wang2024bit,
title={4-bit Shampoo for Memory-Efficient Network Training},
author={Sike Wang and Pan Zhou and Jia Li and Hua Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ASqdVeifn7}
} | Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice.
The states forming the preconditioner and its inverse root restrict the maximum size of models trained by second-order optimizers. To address this, compressing 32-bit optimizer states to lower bitwidths has shown promise in reducing memory usage. However, current approaches only pertain to first-order optimizers. In this paper, we propose the first 4-bit second-order optimizers, exemplified by 4-bit Shampoo, maintaining performance similar to that of 32-bit ones. We show that quantizing the eigenvector matrix of the preconditioner in 4-bit Shampoo is remarkably better than quantizing the preconditioner itself both theoretically and experimentally. By rectifying the orthogonality of the quantized eigenvector matrix, we enhance the approximation of the preconditioner's eigenvector matrix, which also benefits the computation of its inverse 4-th root. Besides, we find that linear square quantization slightly outperforms dynamic tree quantization when quantizing second-order optimizer states. Evaluation on various networks for image classification and natural language modeling demonstrates that our 4-bit Shampoo achieves comparable performance to its 32-bit counterpart while being more memory-efficient. | 4-bit Shampoo for Memory-Efficient Network Training | [
"Sike Wang",
"Pan Zhou",
"Jia Li",
"Hua Huang"
] | NeurIPS.cc/2024/Conference | 2405.18144 | [
"https://github.com/sike-wang/low-bit-shampoo"
] | https://huggingface.co/papers/2405.18144 | 0 | 9 | 1 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=ARV1gJSOzV | @inproceedings{
damrich2024persistent,
title={Persistent Homology for High-dimensional Data Based on Spectral Methods},
author={Sebastian Damrich and Philipp Berens and Dmitry Kobak},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ARV1gJSOzV}
} | Persistent homology is a popular computational tool for analyzing the topology of point clouds, such as the presence of loops or voids. However, many real-world datasets with low intrinsic dimensionality reside in an ambient space of much higher dimensionality. We show that in this case traditional persistent homology becomes very sensitive to noise and fails to detect the correct topology. The same holds true for existing refinements of persistent homology. As a remedy, we find that spectral distances on the k-nearest-neighbor graph of the data, such as diffusion distance and effective resistance, allow to detect the correct topology even in the presence of high-dimensional noise. Moreover, we derive a novel closed-form formula for effective resistance, and describe its relation to diffusion distances. Finally, we apply these methods to high-dimensional single-cell RNA-sequencing data and show that spectral distances allow robust detection of cell cycle loops. | Persistent Homology for High-dimensional Data Based on Spectral Methods | [
"Sebastian Damrich",
"Philipp Berens",
"Dmitry Kobak"
] | NeurIPS.cc/2024/Conference | 2311.03087 | [
"https://github.com/berenslab/eff-ph"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ARLEUVVfTL | @inproceedings{
zheng2024improving,
title={Improving Neural {ODE} Training with Temporal Adaptive Batch Normalization},
author={Su Zheng and Zhengqi Gao and Fan-Keng Sun and Duane S Boning and Bei Yu and Martin D. Wong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ARLEUVVfTL}
} | Neural ordinary differential equations (Neural ODEs) is a family of continuous-depth neural networks where the evolution of hidden states is governed by learnable temporal derivatives. We identify a significant limitation in applying traditional Batch Normalization (BN) to Neural ODEs, due to a fundamental mismatch --- BN was initially designed for discrete neural networks with no temporal dimension, whereas Neural ODEs operate continuously over time. To bridge this gap, we introduce temporal adaptive Batch Normalization (TA-BN), a novel technique that acts as the continuous-time analog to traditional BN. Our empirical findings reveal that TA-BN enables the stacking of more layers within Neural ODEs, enhancing their performance. Moreover, when confined to a model architecture consisting of a single Neural ODE followed by a linear layer, TA-BN achieves 91.1\% test accuracy on CIFAR-10 with 2.2 million parameters, making it the first \texttt{unmixed} Neural ODE architecture to approach MobileNetV2-level parameter efficiency. Extensive numerical experiments on image classification and physical system modeling substantiate the superiority of TA-BN compared to baseline methods. | Improving Neural ODE Training with Temporal Adaptive Batch Normalization | [
"Su Zheng",
"Zhengqi Gao",
"Fan-Keng Sun",
"Duane S Boning",
"Bei Yu",
"Martin D. Wong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ARAxPPIAhq | @inproceedings{
beck2024xlstm,
title={x{LSTM}: Extended Long Short-Term Memory},
author={Maximilian Beck and Korbinian P{\"o}ppel and Markus Spanring and Andreas Auer and Oleksandra Prudnikova and Michael K Kopp and G{\"u}nter Klambauer and Johannes Brandstetter and Sepp Hochreiter},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ARAxPPIAhq}
} | In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale. We now raise a simple question: How far do we get in language modeling when scaling LSTMs to billions of parameters, leveraging the latest techniques from modern LLMs, but mitigating known limitations of LSTMs? Firstly, we introduce exponential gating with appropriate normalization and stabilization techniques. Secondly, we modify the LSTM memory structure, obtaining: (i) sLSTM with a scalar memory, a scalar update, and new memory mixing, (ii) mLSTM that is fully parallelizable with a matrix memory and a covariance update rule. Integrating these LSTM extensions into residual block backbones yields xLSTM blocks that are then residually stacked into xLSTM architectures. Exponential gating and modified memory structures boost xLSTM capabilities to perform favorably when compared to state-of-the-art Transformers and State Space Models, both in performance and scaling. | xLSTM: Extended Long Short-Term Memory | [
"Maximilian Beck",
"Korbinian Pöppel",
"Markus Spanring",
"Andreas Auer",
"Oleksandra Prudnikova",
"Michael K Kopp",
"Günter Klambauer",
"Johannes Brandstetter",
"Sepp Hochreiter"
] | NeurIPS.cc/2024/Conference | 2405.04517 | [
""
] | https://huggingface.co/papers/2405.04517 | 0 | 11 | 0 | 9 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=AQ1umQL7dZ | @inproceedings{
qiao2024model,
title={Model Decides How to Tokenize: Adaptive {DNA} Sequence Tokenization with Mx{DNA}},
author={Lifeng Qiao and Peng Ye and Yuchen Ren and Weiqiang Bai and chaoqi liang and Xinzhu Ma and Nanqing Dong and Wanli Ouyang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AQ1umQL7dZ}
} | Foundation models have made significant strides in understanding the genomic language of DNA sequences. However, previous models typically adopt the tokenization methods designed for natural language, which are unsuitable for DNA sequences due to their unique characteristics. In addition, the optimal approach to tokenize DNA remains largely under-explored, and may not be intuitively understood by humans even if discovered. To address these challenges, we introduce MxDNA, a novel framework where the model autonomously learns an effective DNA tokenization strategy through gradient decent. MxDNA employs a sparse Mixture of Convolution Experts coupled with a deformable convolution to model the tokenization process, with the discontinuous, overlapping, and ambiguous nature of meaningful genomic segments explicitly considered. On Nucleotide Transformer Benchmarks and Genomic Benchmarks, MxDNA demonstrates superior performance to existing methods with less pretraining data and time, highlighting its effectiveness. Finally, we show that MxDNA learns unique tokenization strategy distinct to those of previous methods and captures genomic functionalities at a token level during self-supervised pretraining. Our MxDNA aims to provide a new perspective on DNA tokenization, potentially offering broad applications in various domains and yielding profound insights. Code is available at https://github.com/qiaoqiaoLF/MxDNA. | Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA | [
"Lifeng Qiao",
"Peng Ye",
"Yuchen Ren",
"Weiqiang Bai",
"chaoqi liang",
"Xinzhu Ma",
"Nanqing Dong",
"Wanli Ouyang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=APSBwuMopO | @inproceedings{
nam2024optimized,
title={Optimized Feature Generation for Tabular Data via {LLM}s with Decision Tree Reasoning},
author={Jaehyun Nam and Kyuyoung Kim and Seunghyuk Oh and Jihoon Tack and Jaehyung Kim and Jinwoo Shin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=APSBwuMopO}
} | In tabular prediction tasks, tree-based models combined with automated feature engineering methods often outperform deep learning approaches that rely on learned representations. While these feature engineering techniques are effective, they typically depend on a pre-defined search space and primarily use validation scores for feature selection, thereby missing valuable insights from previous experiments.
To address these limitations, we propose a novel tabular learning framework that utilizes large language models (LLMs), termed Optimizing Column feature generator with decision Tree reasoning (OCTree). Our key idea is to leverage the reasoning capabilities of LLMs to identify effective feature generation rules without manually specifying the search space and provide language-based reasoning information highlighting past experiments as feedback for iterative rule improvements. We use decision trees to convey this reasoning information, as they can be easily represented in natural language, effectively providing knowledge from prior experiments (i.e., the impact of the generated features on performance) to the LLMs. Our empirical results demonstrate that OCTree consistently enhances the performance of various prediction models across diverse benchmarks, outperforming competing automated feature engineering methods. Code is available at https://github.com/jaehyun513/OCTree. | Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning | [
"Jaehyun Nam",
"Kyuyoung Kim",
"Seunghyuk Oh",
"Jihoon Tack",
"Jaehyung Kim",
"Jinwoo Shin"
] | NeurIPS.cc/2024/Conference | 2406.08527 | [
"https://github.com/jaehyun513/octree"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=APBq3KAmFa | @inproceedings{
nakhleh2024a,
title={A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning},
author={Julia B Nakhleh and Joseph Shenouda and Robert D Nowak},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=APBq3KAmFa}
} | This paper studies the properties of solutions to multi-task shallow ReLU neural network learning problems, wherein the network is trained to fit a dataset with minimal sum of squared weights. Remarkably, the solutions learned for each individual task resemble those obtained by solving a kernel method, revealing a novel connection between neural networks and kernel methods. It is known that single-task neural network training problems are equivalent to minimum norm interpolation problem in a non-Hilbertian Banach space, and that the solutions of such problems are generally non-unique. In contrast, we prove that the solutions to univariate-input, multi-task neural network interpolation problems are almost always unique, and coincide with the solution to a minimum-norm interpolation problem in a first-order Sobolev (reproducing kernel) Hilbert Space. We also demonstrate a similar phenomenon in the multivariate-input case; specifically, we show that neural network training problems with a large number of diverse tasks are approximately equivalent to an $\ell^2$ (Hilbert space) minimization problem over a fixed kernel determined by the optimal neurons. | A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning | [
"Julia B Nakhleh",
"Joseph Shenouda",
"Robert D Nowak"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ANO1i9JPtb | @inproceedings{
yang2024buffer,
title={Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models},
author={Ling Yang and Zhaochen Yu and Tianjun Zhang and Shiyi Cao and Minkai Xu and Wentao Zhang and Joseph E. Gonzalez and Bin CUI},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ANO1i9JPtb}
} | We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs). Specifically, we propose meta-buffer to store a series of informative high-level thoughts, namely thought-template, distilled from the problem-solving processes across various tasks. Then for each problem, we retrieve a relevant thought-template and adaptively instantiate it with specific reasoning structures to conduct efficient reasoning. To guarantee the scalability and stability, we further propose buffer-manager to dynamically update the meta-buffer, thus enhancing the capacity of meta-buffer as more tasks are solved. We conduct extensive experiments on 10 challenging reasoning-intensive tasks, and achieve significant performance improvements over previous SOTA methods: 11\% on Game of 24, 20\% on Geometric Shapes and 51\% on Checkmate-in-One. Further analysis demonstrate the superior generalization ability and model robustness of our BoT, while requiring only 12\% of the cost of multi-query prompting methods (e.g., tree/graph of thoughts) on average. Code is available at: https://github.com/YangLing0818/buffer-of-thought-llm | Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models | [
"Ling Yang",
"Zhaochen Yu",
"Tianjun Zhang",
"Shiyi Cao",
"Minkai Xu",
"Wentao Zhang",
"Joseph E. Gonzalez",
"Bin CUI"
] | NeurIPS.cc/2024/Conference | 2406.04271 | [
"https://github.com/yangling0818/buffer-of-thought-llm"
] | https://huggingface.co/papers/2406.04271 | 5 | 28 | 1 | 8 | [
"BitStarWalkin/SuperCorrect-7B",
"QuantFactory/SuperCorrect-7B-GGUF",
"Triangle104/SuperCorrect-7B-Q4_K_S-GGUF",
"Triangle104/SuperCorrect-7B-Q4_K_M-GGUF",
"mav23/SuperCorrect-7B-GGUF",
"Triangle104/SuperCorrect-7B-Q5_K_S-GGUF",
"Triangle104/SuperCorrect-7B-Q5_K_M-GGUF",
"Triangle104/SuperCorrect-7B-Q6_K-GGUF",
"Triangle104/SuperCorrect-7B-Q8_0-GGUF",
"RichardErkhov/BitStarWalkin_-_SuperCorrect-7B-gguf"
] | [] | [] | [
"BitStarWalkin/SuperCorrect-7B",
"QuantFactory/SuperCorrect-7B-GGUF",
"Triangle104/SuperCorrect-7B-Q4_K_S-GGUF",
"Triangle104/SuperCorrect-7B-Q4_K_M-GGUF",
"mav23/SuperCorrect-7B-GGUF",
"Triangle104/SuperCorrect-7B-Q5_K_S-GGUF",
"Triangle104/SuperCorrect-7B-Q5_K_M-GGUF",
"Triangle104/SuperCorrect-7B-Q6_K-GGUF",
"Triangle104/SuperCorrect-7B-Q8_0-GGUF",
"RichardErkhov/BitStarWalkin_-_SuperCorrect-7B-gguf"
] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=ALU676zGFE | @inproceedings{
gupta2024mtgs,
title={{MTGS}: A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction},
author={Anshul Gupta and Samy Tafasca and Arya Farkhondeh and Pierre Vuillecard and Jean-marc Odobez},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ALU676zGFE}
} | Gaze following and social gaze prediction are fundamental tasks providing insights into human communication behaviors, intent, and social interactions. Most previous approaches addressed these tasks separately, either by designing highly specialized social gaze models that do not generalize to other social gaze tasks or by considering social gaze inference as an ad-hoc post-processing of the gaze following task. Furthermore, the vast majority of gaze following approaches have proposed models that can handle only one person at a time and are static, therefore failing to take advantage of social interactions and temporal dynamics. In this paper, we address these limitations and introduce a novel framework to jointly predict the gaze target and social gaze label for all people in the scene. It comprises (i) a temporal, transformer-based architecture that, in addition to frame tokens, handles person-specific tokens capturing the gaze information related to each individual; (ii) a new dataset, VSGaze, built from multiple gaze following and social gaze datasets by extending and validating head detections and tracks, and unifying annotation types. We demonstrate that our model can address and benefit from training on all tasks jointly, achieving state-of-the-art results for multi-person gaze following and social gaze prediction. Our annotations and code will be made publicly available. | MTGS: A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction | [
"Anshul Gupta",
"Samy Tafasca",
"Arya Farkhondeh",
"Pierre Vuillecard",
"Jean-marc Odobez"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ALISPmDPCq | @inproceedings{
dekoninck2024constat,
title={ConStat: Performance-Based Contamination Detection in Large Language Models},
author={Jasper Dekoninck and Mark Niklas Mueller and Martin Vechev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ALISPmDPCq}
} | Public benchmarks play an essential role in the evaluation of large language models. However, data contamination can lead to inflated performance, rendering them unreliable for model comparison. It is therefore crucial to detect contamination and estimate its impact on measured performance. Unfortunately, existing detection methods can be easily evaded and fail to quantify contamination. To overcome these limitations, we propose a novel definition of *contamination as artificially inflated and non-generalizing benchmark performance* instead of the inclusion of benchmark samples in the training data. This perspective enables us to detect *any* model with inflated performance, i.e., performance that does not generalize to rephrased samples, synthetic samples from the same distribution, or different benchmarks for the same task. Based on this insight, we develop ConStat, a statistical method that reliably detects and quantifies contamination by comparing performance between a primary and reference benchmark relative to a set of reference models. We demonstrate the effectiveness of ConStat in an extensive evaluation of diverse model architectures, benchmarks, and contamination scenarios and find high levels of contamination in multiple popular models including Mistral, Llama, Yi, and the top-3 Open LLM Leaderboard models. | ConStat: Performance-Based Contamination Detection in Large Language Models | [
"Jasper Dekoninck",
"Mark Niklas Mueller",
"Martin Vechev"
] | NeurIPS.cc/2024/Conference | 2405.16281 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AKBTFQhCjm | @inproceedings{
denker2024deft,
title={{DEFT}: Efficient Fine-tuning of Diffusion Models by Learning the Generalised \$h\$-transform},
author={Alexander Denker and Francisco Vargas and Shreyas Padhy and Kieran Didi and Simon V Mathis and Riccardo Barbano and Vincent Dutordoir and Emile Mathieu and Urszula Julia Komorowska and Pietro Lio},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AKBTFQhCjm}
} | Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems.
In many real-world applications, we often have access to large, expensively trained unconditional diffusion models, which we aim to exploit for improving conditional sampling.
Most recent approaches are motivated heuristically and lack a unifying framework, obscuring connections between them. Further, they often suffer from issues such as being very sensitive to hyperparameters, being expensive to train or needing access to weights hidden behind a closed API. In this work, we unify conditional training and sampling using the mathematically well-understood Doob's h-transform. This new perspective allows us to unify many existing methods under a common umbrella. Under this framework, we propose DEFT (Doob's h-transform Efficient FineTuning), a new approach for conditional generation that simply fine-tunes a very small network to quickly learn the conditional $h$-transform, while keeping the larger unconditional network unchanged. DEFT is much faster than existing baselines while achieving state-of-the-art performance across a variety of linear and non-linear benchmarks. On image reconstruction tasks, we achieve speedups of up to 1.6$\times$, while having the best perceptual quality on natural images and reconstruction performance on medical images. Further, we also provide initial experiments on protein motif scaffolding and outperform reconstruction guidance methods. | DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised h-transform | [
"Alexander Denker",
"Francisco Vargas",
"Shreyas Padhy",
"Kieran Didi",
"Simon V Mathis",
"Riccardo Barbano",
"Vincent Dutordoir",
"Emile Mathieu",
"Urszula Julia Komorowska",
"Pietro Lio"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/alexdenker/deft"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=AH5KwUSsln | @inproceedings{
caprio2024credal,
title={Credal Learning Theory},
author={Michele Caprio and Maryam Sultana and Eleni Elia and Fabio Cuzzolin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AH5KwUSsln}
} | Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution. In actual deployment, however, the data distribution may (and often does) vary, causing domain adaptation/generalization issues. In this paper we lay the foundations for a `credal' theory of learning, using convex sets of probabilities (credal sets) to model the variability in the data-generating distribution. Such credal sets, we argue, may be inferred from a finite sample of training sets. Bounds are derived for the case of finite hypotheses spaces (both assuming realizability or not), as well as infinite model spaces, which directly generalize classical results. | Credal Learning Theory | [
"Michele Caprio",
"Maryam Sultana",
"Eleni Elia",
"Fabio Cuzzolin"
] | NeurIPS.cc/2024/Conference | 2402.00957 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AH1mFs3c7o | @inproceedings{
wang2024intercontrol,
title={InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint},
author={Zhenzhi Wang and Jingbo Wang and Yixuan Li and Dahua Lin and Bo Dai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AH1mFs3c7o}
} | Text-conditioned motion synthesis has made remarkable progress with the emergence of diffusion models. However, the majority of these motion diffusion models are primarily designed for a single character and overlook multi-human interactions. In our approach, we strive to explore this problem by synthesizing human motion with interactions for a group of characters of any size in a zero-shot manner. The key aspect of our approach is the adaptation of human-wise interactions as pairs of human joints that can be either in contact or separated by a desired distance. In contrast to existing methods that necessitate training motion generation models on multi-human motion datasets with a fixed number of characters, our approach inherently possesses the flexibility to model human interactions involving an arbitrary number of individuals, thereby transcending the limitations imposed by the training data. We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. It consists of a motion controller and an inverse kinematics guidance module that realistically and accurately aligns the joints of synthesized characters to the desired location. Furthermore, we demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model (LLM). Experimental results highlight the capability of our framework to generate interactions with multiple human characters and its potential to work with off-the-shelf physics-based character simulators. Code is available at https://github.com/zhenzhiwang/intercontrol. | InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint | [
"Zhenzhi Wang",
"Jingbo Wang",
"Yixuan Li",
"Dahua Lin",
"Bo Dai"
] | NeurIPS.cc/2024/Conference | 2311.15864 | [
"https://github.com/zhenzhiwang/intercontrol"
] | https://huggingface.co/papers/2311.15864 | 1 | 0 | 0 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=AFnSMlye5K | @inproceedings{
su2024disentangling,
title={Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis},
author={Jiayu Su and David A. Knowles and Raul Rabadan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AFnSMlye5K}
} | The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive deep learning approaches lack interpretability. Here, we introduce Supervised Independent Subspace Principal Component Analysis ($\texttt{sisPCA}$), a PCA extension designed for multi-subspace learning. Leveraging the Hilbert-Schmidt Independence Criterion (HSIC), $\texttt{sisPCA}$ incorporates supervision and simultaneously ensures subspace disentanglement. We demonstrate $\texttt{sisPCA}$'s connections with autoencoders and regularized linear regression and showcase its ability to identify and separate hidden data structures through extensive applications, including breast cancer diagnosis from image features, learning aging-associated DNA methylation changes, and single-cell analysis of malaria infection. Our results reveal distinct functional pathways associated with malaria colonization, underscoring the essentiality of explainable representation in high-dimensional data analysis. | Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis | [
"Jiayu Su",
"David A. Knowles",
"Raul Rabadan"
] | NeurIPS.cc/2024/Conference | 2410.23595 | [
"https://github.com/JiayuSuPKU/sispca"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AF32GbuupC | @inproceedings{
luo2024fast,
title={Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification},
author={Yihong Luo and Yuhan Chen and Siya Qiu and Yiwei Wang and Chen Zhang and Yan Zhou and Xiaochun Cao and Jing Tang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AF32GbuupC}
} | Graph Neural Networks (GNNs) have shown superior performance in node classification. However, GNNs perform poorly in the Few-Shot Node Classification (FSNC) task that requires robust generalization to make accurate predictions for unseen classes with limited labels. To tackle the challenge, we propose the integration of Sharpness-Aware Minimization (SAM)--a technique designed to enhance model generalization by finding a flat minimum of the loss landscape--into GNN training. The standard SAM approach, however, consists of two forward-backward steps in each training iteration, doubling the computational cost compared to the base optimizer (e.g., Adam). To mitigate this drawback, we introduce a novel algorithm, Fast Graph Sharpness-Aware Minimization (FGSAM), that integrates the rapid training of Multi-Layer Perceptrons (MLPs) with the superior performance of GNNs. Specifically, we utilize GNNs for parameter perturbation while employing MLPs to minimize the perturbed loss so that we can find a flat minimum with good generalization more efficiently. Moreover, our method reutilizes the gradient from the perturbation phase to incorporate graph topology into the minimization process at almost zero additional cost. To further enhance training efficiency, we develop FGSAM+ that executes exact perturbations periodically. Extensive experiments demonstrate that our proposed algorithm outperforms the standard SAM with lower computational costs in FSNC tasks. In particular, our FGSAM+ as a SAM variant offers a faster optimization than the base optimizer in most cases. In addition to FSNC, our proposed methods also demonstrate competitive performance in the standard node classification task for heterophilic graphs, highlighting the broad applicability. | Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification | [
"Yihong Luo",
"Yuhan Chen",
"Siya Qiu",
"Yiwei Wang",
"Chen Zhang",
"Yan Zhou",
"Xiaochun Cao",
"Jing Tang"
] | NeurIPS.cc/2024/Conference | 2410.16845 | [
"https://github.com/draym28/fgsam_neurips24"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ADV0Pzi3Ol | @inproceedings{
li2024beyond,
title={Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales},
author={Tang Li and Mengmeng Ma and Xi Peng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ADV0Pzi3Ol}
} | Large pretrained foundation models demonstrate exceptional performance and, in some high-stakes applications, even surpass human experts. However, most of these models are currently evaluated primarily on prediction accuracy, overlooking the validity of the rationales behind their accurate predictions. For the safe deployment of foundation models, there is a pressing need to ensure *double-correct predictions*, *i.e.*, correct prediction backed by correct rationales. To achieve this, we propose a two-phase scheme: First, we curate a new dataset that offers structured rationales for visual recognition tasks. Second, we propose a rationale-informed optimization method to guide the model in disentangling and localizing visual evidence for each rationale, without requiring manual annotations. Extensive experiments and ablation studies demonstrate that our model outperforms state-of-the-art models by up to 10.1\% in prediction accuracy across a wide range of tasks. Furthermore, our method significantly improves the model's rationale correctness, improving localization by 7.5\% and disentanglement by 36.5\%. Our dataset, source code, and pretrained weights: https://github.com/deep-real/DCP | Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales | [
"Tang Li",
"Mengmeng Ma",
"Xi Peng"
] | NeurIPS.cc/2024/Conference | 2411.00132 | [
"https://github.com/deep-real/dcp"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ADJASE9uQ2 | @inproceedings{
liu2024dquant,
title={2{DQ}uant: Low-bit Post-Training Quantization for Image Super-Resolution},
author={Kai Liu and Haotong Qin and Yong Guo and Xin Yuan and Linghe Kong and Guihai Chen and Yulun Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ADJASE9uQ2}
} | Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit parameters and efficient integer/bitwise constructions for storage compression and inference acceleration, respectively. However, it is notorious that low-bit quantization degrades the accuracy of SR models compared to their full-precision (FP) counterparts. Despite several efforts to alleviate the degradation, the transformer-based SR model still suffers severe degradation due to its distinctive activation distribution. In this work, we present a dual-stage low-bit post-training quantization (PTQ) method for image super-resolution, namely 2DQuant, which achieves efficient and accurate SR under low-bit quantization. The proposed method first investigates the weight and activation and finds that the distribution is characterized by coexisting symmetry and asymmetry, long tails. Specifically, we propose Distribution-Oriented Bound Initialization (DOBI), using different searching strategies to search a coarse bound for quantizers. To obtain refined quantizer parameters, we further propose Distillation Quantization Calibration (DQC), which employs a distillation approach to make the quantized model learn from its FP counterpart. Through extensive experiments on different bits and scaling factors, the performance of DOBI can reach the state-of-the-art (SOTA) while after stage two, our method surpasses existing PTQ in both metrics and visual effects. 2DQuant gains an increase in PSNR as high as 4.52dB on Set5 (x2) compared with SOTA when quantized to 2-bit and enjoys a 3.60x compression ratio and 5.08x speedup ratio. The code and models are available at https://github.com/Kai-Liu001/2DQuant. | 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution | [
"Kai Liu",
"Haotong Qin",
"Yong Guo",
"Xin Yuan",
"Linghe Kong",
"Guihai Chen",
"Yulun Zhang"
] | NeurIPS.cc/2024/Conference | 2406.06649 | [
"https://github.com/kai-liu001/2dquant"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ACIDDnTbSJ | @inproceedings{
liu2024feint,
title={Feint Behaviors and Strategies: Formalization, Implementation and Evaluation},
author={Junyu Liu and Xiangjun Peng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ACIDDnTbSJ}
} | Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors, and their implications on game strategies. In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games. The key idea of our work is to (1) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual-Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies, in terms of temporal, spatial, and their collective impacts respectively; and (3) provide a unified implementation scheme of Feint behaviors in existing MARL frameworks. The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption. | Feint Behaviors and Strategies: Formalization, Implementation and Evaluation | [
"Junyu Liu",
"Xiangjun Peng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ACCqGLviig | @inproceedings{
jiao2024vector,
title={Vector Quantization Prompting for Continual Learning},
author={Li Jiao and Qiuxia Lai and YU LI and Qiang Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ACCqGLviig}
} | Continual learning requires to overcome catastrophic forgetting when training a single model on a sequence of tasks. Recent top-performing approaches are prompt-based methods that utilize a set of learnable parameters (i.e., prompts) to encode task knowledge, from which appropriate ones are selected to guide the fixed pre-trained model in generating features tailored to a certain task. However, existing methods rely on predicting prompt identities for prompt selection, where the identity prediction process cannot be optimized with task loss. This limitation leads to sub-optimal prompt selection and inadequate adaptation of pre-trained features for a specific task. Previous efforts have tried to address this by directly generating prompts from input queries instead of selecting from a set of candidates. However, these prompts are continuous, which lack sufficient abstraction for task knowledge representation, making them less effective for continual learning. To address these challenges, we propose VQ-Prompt, a prompt-based continual learning method that incorporates Vector Quantization (VQ) into end-to-end training of a set of discrete prompts. In this way, VQ-Prompt can optimize the prompt selection process with task loss and meanwhile achieve effective abstraction of task knowledge for continual learning. Extensive experiments show that VQ-Prompt outperforms state-of-the-art continual learning methods across a variety of benchmarks under the challenging class-incremental setting. | Vector Quantization Prompting for Continual Learning | [
"Li Jiao",
"Qiuxia Lai",
"YU LI",
"Qiang Xu"
] | NeurIPS.cc/2024/Conference | 2410.20444 | [
"https://github.com/jiaolifengmi/vq-prompt"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ABYdKpDb8p | @inproceedings{
vyas2024learning,
title={Learning Transferable Features for Implicit Neural Representations},
author={Kushal Vyas and Ahmed Imtiaz Humayun and Aniket Dashpute and Richard Baraniuk and Ashok Veeraraghavan and Guha Balakrishnan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ABYdKpDb8p}
} | Implicit neural representations (INRs) have demonstrated success in a variety of applications, including inverse problems and neural rendering. An INR is typically trained to capture one signal of interest, resulting in learned neural features that are highly attuned to that signal. Assumed to be less generalizable, we explore the aspect of transferability of such learned neural features for fitting similar signals. We introduce a new INR training framework, STRAINER that learns transferable features for fitting INRs to new signals from a given distribution, faster and with better reconstruction quality. Owing to the sequential layer-wise affine operations in an INR, we propose to learn transferable representations by sharing initial encoder layers across multiple INRs with independent decoder layers. At test time, the learned encoder representations are transferred as initialization for an otherwise randomly initialized INR. We find STRAINER to yield extremely powerful initialization for fitting images from the same domain and allow for a ≈ +10dB gain in signal quality early on compared to an untrained INR itself. STRAINER also provides a simple way to encode data-driven priors in INRs. We evaluate STRAINER on multiple in-domain and out-of-domain signal fitting tasks and inverse problems and further provide detailed analysis and discussion on the transferability of STRAINER’s features. | Learning Transferable Features for Implicit Neural Representations | [
"Kushal Vyas",
"Ahmed Imtiaz Humayun",
"Aniket Dashpute",
"Richard Baraniuk",
"Ashok Veeraraghavan",
"Guha Balakrishnan"
] | NeurIPS.cc/2024/Conference | 2409.09566 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=AB6XpMzvqH | @inproceedings{
agarwal2024manyshot,
title={Many-Shot In-Context Learning},
author={Rishabh Agarwal and Avi Singh and Lei M Zhang and Bernd Bohnet and Luis Rosias and Stephanie C.Y. Chan and Biao Zhang and Ankesh Anand and Zaheer Abbas and Azade Nova and John D Co-Reyes and Eric Chu and Feryal Behbahani and Aleksandra Faust and Hugo Larochelle},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=AB6XpMzvqH}
} | Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples – the many-shot regime. Going from few-shot to many-shot, we observe significant performance gains across a wide variety of generative and discriminative tasks. While promising, many-shot ICL can be bottlenecked by the available amount of human-generated outputs. To mitigate this limitation, we explore two new settings: (1) "Reinforced ICL" that uses model-generated chain-of-thought rationales in place of human rationales, and (2) "Unsupervised ICL" where we remove rationales from the prompt altogether, and prompts the model only with domain-specific inputs. We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks. We demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases, can learn high-dimensional functions with numerical inputs, and performs comparably to supervised fine-tuning. Finally, we reveal the limitations of next-token prediction loss as an indicator of downstream ICL performance. | Many-Shot In-Context Learning | [
"Rishabh Agarwal",
"Avi Singh",
"Lei M Zhang",
"Bernd Bohnet",
"Luis Rosias",
"Stephanie C.Y. Chan",
"Biao Zhang",
"Ankesh Anand",
"Zaheer Abbas",
"Azade Nova",
"John D Co-Reyes",
"Eric Chu",
"Feryal Behbahani",
"Aleksandra Faust",
"Hugo Larochelle"
] | NeurIPS.cc/2024/Conference | 2404.11018 | [
""
] | https://huggingface.co/papers/2404.11018 | 2 | 4 | 0 | 13 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=A969ouPqEs | @inproceedings{
chen2024difflight,
title={DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data},
author={Hanyang Chen and Yang Jiang and Shengnan Guo and Xiaowei Mao and Youfang Lin and Huaiyu Wan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A969ouPqEs}
} | The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC assume that traffic data from all surrounding intersections is fully and continuously available through sensors. In real-world applications, this assumption often fails due to sensor malfunctions or data loss, making TSC with missing data a critical challenge. To meet the needs of practical applications, we introduce DiffLight, a novel conditional diffusion model for TSC under data-missing scenarios in the offline setting. Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards from interfering with the learning process. Meanwhile, to effectively capture the spatial-temporal dependencies among intersections, we design a Spatial-Temporal transFormer (STFormer) architecture. In addition, we propose a Diffusion Communication Mechanism (DCM) to promote better communication and control performance under data-missing scenarios. Extensive experiments on five datasets with various data-missing scenarios demonstrate that DiffLight is an effective controller to address TSC with missing data. The code of DiffLight is released at https://github.com/lokol5579/DiffLight-release. | DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data | [
"Hanyang Chen",
"Yang Jiang",
"Shengnan Guo",
"Xiaowei Mao",
"Youfang Lin",
"Huaiyu Wan"
] | NeurIPS.cc/2024/Conference | 2410.22938 | [
"https://github.com/lokol5579/DiffLight-release"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=A7wC1CTkYl | @inproceedings{
prabhu2024efficient,
title={Efficient Lifelong Model Evaluation in an Era of Rapid Progress},
author={Ameya Prabhu and Vishaal Udandarao and Philip Torr and Matthias Bethge and Adel Bibi and Samuel Albanie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A7wC1CTkYl}
} | Standardized benchmarks drive progress in machine learning. However, with repeated testing, the risk of overfitting grows as algorithms over-exploit benchmark idiosyncrasies. In our work, we seek to mitigate this challenge by compiling \textit{ever-expanding} large-scale benchmarks called \textit{Lifelong Benchmarks}. As exemplars of our approach, we create \textit{Lifelong-CIFAR10} and \textit{Lifelong-ImageNet}, containing (for now) 1.69M and 1.98M test samples, respectively. While reducing overfitting, lifelong benchmarks introduce a key challenge: the high cost of evaluating a growing number of models across an ever-expanding sample set. To address this challenge, we also introduce an efficient evaluation framework: \textit{Sort \& Search (S\&S)}, which reuses previously evaluated models by leveraging dynamic programming algorithms to selectively rank and sub-select test samples, enabling cost-effective lifelong benchmarking. Extensive empirical evaluations across $\sim$31,000 models demonstrate that \textit{S\&S} achieves highly-efficient approximate accuracy measurement, reducing compute cost from 180 GPU days to 5 GPU hours ($\sim$1000x reduction) on a single A100 GPU, with low approximation error. As such, lifelong benchmarks offer a robust, practical solution to the ``benchmark exhaustion'' problem. | Efficient Lifelong Model Evaluation in an Era of Rapid Progress | [
"Ameya Prabhu",
"Vishaal Udandarao",
"Philip Torr",
"Matthias Bethge",
"Adel Bibi",
"Samuel Albanie"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=A5pabdZp2F | @inproceedings{
dong2024multiood,
title={Multi{OOD}: Scaling Out-of-Distribution Detection for Multiple Modalities},
author={Hao Dong and Yue Zhao and Eleni Chatzi and Olga Fink},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A5pabdZp2F}
} | Detecting out-of-distribution (OOD) samples is important for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery. Existing research has mainly focused on unimodal scenarios on image data. However, real-world applications are inherently multimodal, which makes it essential to leverage information from multiple modalities to enhance the efficacy of OOD detection. To establish a foundation for more realistic Multimodal OOD Detection, we introduce the first-of-its-kind benchmark, MultiOOD, characterized by diverse dataset sizes and varying modality combinations. We first evaluate existing unimodal OOD detection algorithms on MultiOOD, observing that the mere inclusion of additional modalities yields substantial improvements. This underscores the importance of utilizing multiple modalities for OOD detection. Based on the observation of Modality Prediction Discrepancy between in-distribution (ID) and OOD data, and its strong correlation with OOD performance, we propose the Agree-to-Disagree (A2D) algorithm to encourage such discrepancy during training. Moreover, we introduce a novel outlier synthesis method, NP-Mix, which explores broader feature spaces by leveraging the information from nearest neighbor classes and complements A2D to strengthen OOD detection performance. Extensive experiments on MultiOOD demonstrate that training with A2D and NP-Mix improves existing OOD detection algorithms by a large margin. To support accessibility and reproducibility, our source code and MultiOOD benchmark are available at https://github.com/donghao51/MultiOOD. | MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities | [
"Hao Dong",
"Yue Zhao",
"Eleni Chatzi",
"Olga Fink"
] | NeurIPS.cc/2024/Conference | 2405.17419 | [
"https://github.com/donghao51/multiood"
] | https://huggingface.co/papers/2405.17419 | 0 | 0 | 0 | 4 | [] | [
"hdong51/MultiOOD"
] | [] | [] | [
"hdong51/MultiOOD"
] | [] | 1 | oral |
null | https://openreview.net/forum?id=A3jHvChR8K | @inproceedings{
xu2024semiopen,
title={Semi-Open 3D Object Retrieval via Hierarchical Equilibrium on Hypergraph},
author={Yang Xu and Yifan Feng and Jun Zhang and Jun-Hai Yong and Yue Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A3jHvChR8K}
} | Existing open-set learning methods consider only the single-layer labels of objects and strictly assume no overlap between the training and testing sets, leading to contradictory optimization for superposed categories. In this paper, we introduce a more practical Semi-Open Environment setting for open-set 3D object retrieval with hierarchical labels, in which the training and testing set share a partial label space for coarse categories but are completely disjoint from fine categories. We propose the Hypergraph-Based Hierarchical Equilibrium Representation (HERT) framework for this task. Specifically, we propose the Hierarchical Retrace Embedding (HRE) module to overcome the global disequilibrium of unseen categories by fully leveraging the multi-level category information. Besides, tackling the feature overlap and class confusion problem, we perform the Structured Equilibrium Tuning (SET) module to utilize more equilibrial correlations among objects and generalize to unseen categories, by constructing a superposed hypergraph based on the local coherent and global entangled correlations. Furthermore, we generate four semi-open 3DOR datasets with multi-level labels for benchmarking. Results demonstrate that the proposed method can effectively generate the hierarchical embeddings of 3D objects and generalize them towards semi-open environments. | Semi-Open 3D Object Retrieval via Hierarchical Equilibrium on Hypergraph | [
"Yang Xu",
"Yifan Feng",
"Jun Zhang",
"Jun-Hai Yong",
"Yue Gao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=A3hxp0EeNW | @inproceedings{
madeira2024generative,
title={Generative Modelling of Structurally Constrained Graphs},
author={Manuel Madeira and Clement Vignac and Dorina Thanou and Pascal Frossard},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A3hxp0EeNW}
} | Graph diffusion models have emerged as state-of-the-art techniques in graph generation; yet, integrating domain knowledge into these models remains challenging.
Domain knowledge is particularly important in real-world scenarios, where invalid generated graphs hinder deployment in practical applications.
Unconstrained and conditioned graph diffusion models fail to guarantee such domain-specific structural properties.
We present ConStruct, a novel framework that enables graph diffusion models to incorporate hard constraints on specific properties, such as planarity or acyclicity.
Our approach ensures that the sampled graphs remain within the domain of graphs that satisfy the specified property throughout the entire trajectory in both the forward and reverse processes. This is achieved by introducing an edge-absorbing noise model and a new projector operator.
ConStruct demonstrates versatility across several structural and edge-deletion invariant constraints and achieves state-of-the-art performance for both synthetic benchmarks and attributed real-world datasets.
For example, by incorporating planarity constraints in digital pathology graph datasets, the proposed method outperforms existing baselines, improving data validity by up to 71.1 percentage points. | Generative Modelling of Structurally Constrained Graphs | [
"Manuel Madeira",
"Clement Vignac",
"Dorina Thanou",
"Pascal Frossard"
] | NeurIPS.cc/2024/Conference | 2406.17341 | [
"https://github.com/manuelmlmadeira/ConStruct"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=A34sBX4R5N | @inproceedings{
li2024optimal,
title={Optimal Transport-based Labor-free Text Prompt Modeling for Sketch Re-identification},
author={Rui Li and Tingting Ren and Jie Wen and Jinxing Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A34sBX4R5N}
} | Sketch Re-identification (Sketch Re-ID),
which aims to retrieve target person from an image gallery based on a sketch query, is crucial for criminal investigation, law enforcement, and missing person searches.
Existing methods aim to alleviate the modality gap by employing semantic metrics constraints or auxiliary modal guidance. However, they incur expensive labor costs and inevitably omit fine-grained modality-consistent information due to the abstraction of sketches.
To address this issue, this paper proposes a novel $\textit{Optimal Transport-based Labor-free Text Prompt Modeling}$ (OLTM) network, which hierarchically extracts coarse- and fine-grained similarity representations guided by textual semantic information without any additional annotations.
Specifically, multiple target attributes are flexibly obtained by a pre-trained visual question answering (VQA) model. Subsequently, a text prompt reasoning module employs learnable prompt strategy and optimal transport algorithm to extract discriminative global and local text representations, which serve as a bridge for hierarchical and multi-granularity modal alignment between sketch and image modalities.
Additionally, instead of measuring the similarity of two samples by only computing their distance, a novel triplet assignment loss is further proposed, in which the whole data distribution also contributes to optimizing the inter/intra-class distances. Extensive experiments conducted on two public benchmarks consistently demonstrate the robustness and superiority of our OLTM over state-of-the-art methods. | Optimal Transport-based Labor-free Text Prompt Modeling for Sketch Re-identification | [
"Rui Li",
"Tingting Ren",
"Jie Wen",
"Jinxing Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=A0cok1GK9c | @inproceedings{
kachaiev2024learning,
title={Learning to Embed Distributions via Maximum Kernel Entropy},
author={Oleksii Kachaiev and Stefano Recanatesi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A0cok1GK9c}
} | Empirical data can often be considered as samples from a set of probability distributions. Kernel methods have emerged as a natural approach for learning to classify these distributions. Although numerous kernels between distributions have been proposed, applying kernel methods to distribution regression tasks remains challenging, primarily because selecting a suitable kernel is not straightforward. Surprisingly, the question of learning a data-dependent distribution kernel has received little attention. In this paper, we propose a novel objective for the unsupervised learning of data-dependent distribution kernel, based on the principle of entropy maximization in the space of probability measure embeddings. We examine the theoretical properties of the latent embedding space induced by our objective, demonstrating that its geometric structure is well-suited for solving downstream discriminative tasks. Finally, we demonstrate the performance of the learned kernel across different modalities. | Learning to Embed Distributions via Maximum Kernel Entropy | [
"Oleksii Kachaiev",
"Stefano Recanatesi"
] | NeurIPS.cc/2024/Conference | 2408.00549 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=A0HSmrwtLH | @inproceedings{
teneggi2024testing,
title={Testing Semantic Importance via Betting},
author={Jacopo Teneggi and Jeremias Sulam},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=A0HSmrwtLH}
} | Recent works have extended notions of feature importance to semantic concepts that are inherently interpretable to the users interacting with a black-box predictive model. Yet, precise statistical guarantees such as false positive rate and false discovery rate control are needed to communicate findings transparently, and to avoid unintended consequences in real-world scenarios. In this paper, we formalize the global (i.e., over a population) and local (i.e., for a sample) statistical importance of semantic concepts for the predictions of opaque models by means of conditional independence, which allows for rigorous testing. We use recent ideas of sequential kernelized independence testing to induce a rank of importance across concepts, and we showcase the effectiveness and flexibility of our framework on synthetic datasets as well as on image classification using several vision-language models. | Testing Semantic Importance via Betting | [
"Jacopo Teneggi",
"Jeremias Sulam"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/Sulam-Group/IBYDMT"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9zQl27mqWE | @inproceedings{
tu2024mixed,
title={Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes},
author={Zhenfeng Tu and Santiago Aranguri and Arthur Jacot},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9zQl27mqWE}
} | The training dynamics of linear networks are well studied in two distinct
setups: the lazy regime and balanced/active regime, depending on the
initialization and width of the network. We provide a surprisingly
simple unifying formula for the evolution of the learned matrix that
contains as special cases both lazy and balanced regimes but also
a mixed regime in between the two. In the mixed regime, a part of
the network is lazy while the other is balanced. More precisely the
network is lazy along singular values that are below a certain threshold
and balanced along those that are above the same threshold. At initialization,
all singular values are lazy, allowing for the network to align itself
with the task, so that later in time, when some of the singular value
cross the threshold and become active they will converge rapidly (convergence
in the balanced regime is notoriously difficult in the absence of
alignment). The mixed regime is the `best of both worlds': it converges
from any random initialization (in contrast to balanced dynamics which
require special initialization), and has a low rank bias (absent in
the lazy dynamics). This allows us to prove an almost complete phase
diagram of training behavior as a function of the variance at initialization
and the width, for a MSE training task. | Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes | [
"Zhenfeng Tu",
"Santiago Aranguri",
"Arthur Jacot"
] | NeurIPS.cc/2024/Conference | 2405.17580 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9xoFciqYIU | @inproceedings{
yang2024attention,
title={Attention boosted Individualized Regression},
author={Guang Yang and Yuan Cao and Long Feng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9xoFciqYIU}
} | Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model. | Attention boosted Individualized Regression | [
"Guang Yang",
"Yuan Cao",
"Long Feng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9wtlfRKwZS | @inproceedings{
gao2024global,
title={Global Convergence in Training Large-Scale Transformers},
author={Cheng Gao and Yuan Cao and Zihao Li and Yihan He and Mengdi Wang and Han Liu and Jason Matthew Klusowski and Jianqing Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9wtlfRKwZS}
} | Despite the widespread success of Transformers across various domains, their optimization guarantees in large-scale model settings are not well-understood. This paper rigorously analyzes the convergence properties of gradient flow in training Transformers with weight decay regularization. First, we construct the mean-field limit of large-scale Transformers, showing that as the model width and depth go to infinity, gradient flow converges to the Wasserstein gradient flow, which is represented by a partial differential equation. Then, we demonstrate that the gradient flow reaches a global minimum consistent with the PDE solution when the weight decay regularization parameter is sufficiently small. Our analysis is based on a series of novel mean-field techniques that adapt to Transformers. Compared with existing tools for deep networks (Lu et al., 2020) that demand homogeneity and global Lipschitz smoothness, we utilize a refined analysis assuming only $\textit{partial homogeneity}$ and $\textit{local Lipschitz smoothness}$. These new techniques may be of independent interest. | Global Convergence in Training Large-Scale Transformers | [
"Cheng Gao",
"Yuan Cao",
"Zihao Li",
"Yihan He",
"Mengdi Wang",
"Han Liu",
"Jason Matthew Klusowski",
"Jianqing Fan"
] | NeurIPS.cc/2024/Conference | 2410.23610 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9vcqleAHPl | @inproceedings{
fu2024fast,
title={{FAST}: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification},
author={Kexue Fu and xiaoyuan Luo and Linhao Qu and Shuo Wang and Ying Xiong and Ilias Maglogiannis and Longxiang Gao and Manning Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9vcqleAHPl}
} | The expensive fine-grained annotation and data scarcity
have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://github.com/fukexue/FAST. | FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification | [
"Kexue Fu",
"xiaoyuan Luo",
"Linhao Qu",
"Shuo Wang",
"Ying Xiong",
"Ilias Maglogiannis",
"Longxiang Gao",
"Manning Wang"
] | NeurIPS.cc/2024/Conference | 2409.19720 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9utMGIbHBt | @inproceedings{
abu-hussein2024udpm,
title={{UDPM}: Upsampling Diffusion Probabilistic Models},
author={Shady Abu-Hussein and Raja Giryes},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9utMGIbHBt}
} | Denoising Diffusion Probabilistic Models (DDPM) have recently gained significant attention. DDPMs compose a Markovian process that begins in the data domain and gradually adds noise until reaching pure white noise. DDPMs generate high-quality samples from complex data distributions by defining an inverse process and training a deep neural network to learn this mapping. However, these models are inefficient because they require many diffusion steps to produce aesthetically pleasing samples. Additionally, unlike generative adversarial networks (GANs), the latent space of diffusion models is less interpretable. In this work, we propose to generalize the denoising diffusion process into an Upsampling Diffusion Probabilistic Model (UDPM). In the forward process, we reduce the latent variable dimension through downsampling, followed by the traditional noise perturbation. As a result, the reverse process gradually denoises and upsamples the latent variable to produce a sample from the data distribution. We formalize the Markovian diffusion processes of UDPM and demonstrate its generation capabilities on the popular FFHQ, AFHQv2, and CIFAR10 datasets. UDPM generates images with as few as three network evaluations, whose overall computational cost is less than a single DDPM or EDM step while achieving an FID score of 6.86. This surpasses current state-of-the-art efficient diffusion models that use a single denoising step for sampling. Additionally, UDPM offers an interpretable and interpolable latent space, which gives it an advantage over traditional DDPMs. Our code is available online: \url{https://github.com/shadyabh/UDPM/} | UDPM: Upsampling Diffusion Probabilistic Models | [
"Shady Abu-Hussein",
"Raja Giryes"
] | NeurIPS.cc/2024/Conference | 2305.16269 | [
"https://github.com/shadyabh/udpm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9uolDxbYLm | @inproceedings{
dissanayake2024model,
title={Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory},
author={Pasan Dissanayake and Sanghamitra Dutta},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9uolDxbYLm}
} | Counterfactual explanations provide ways of achieving a favorable model outcome with minimum input perturbation. However, counterfactual explanations can also be leveraged to reconstruct the model by strategically training a surrogate model to give similar predictions as the original (target) model. In this work, we analyze how model reconstruction using counterfactuals can be improved by
further leveraging the fact that the counterfactuals also lie quite close to the decision boundary. Our main contribution is to derive novel theoretical relationships between the error in model reconstruction and the number of counterfactual queries required using polytope theory. Our theoretical analysis leads us to propose a strategy for model reconstruction that we call Counterfactual Clamping Attack (CCA) which trains a surrogate model using a unique loss function that treats counterfactuals differently than ordinary instances. Our approach also alleviates the related problem of decision boundary shift that arises in existing model reconstruction approaches when counterfactuals are treated as ordinary instances. Experimental results demonstrate that our strategy improves fidelity between the target and surrogate model predictions on several datasets. | Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory | [
"Pasan Dissanayake",
"Sanghamitra Dutta"
] | NeurIPS.cc/2024/Conference | 2405.05369 | [
"https://github.com/pasandissanayake/model-reconstruction-using-counterfactuals"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9uMJeCUeKk | @inproceedings{
zeng2024ask,
title={Ask, Attend, Attack: An Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models},
author={Qingyuan Zeng and Zhenzhong Wang and Yiu-ming Cheung and Min Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9uMJeCUeKk}
} | While image-to-text models have demonstrated significant advancements in various vision-language tasks, they remain susceptible to adversarial attacks. Existing white-box attacks on image-to-text models require access to the architecture, gradients, and parameters of the target model, resulting in low practicality. Although the recently proposed gray-box attacks have improved practicality, they suffer from semantic loss during the training process, which limits their targeted attack performance. To advance adversarial attacks of image-to-text models, this paper focuses on a challenging scenario: decision-based black-box targeted attacks where the attackers only have access to the final output text and aim to perform targeted attacks. Specifically, we formulate the decision-based black-box targeted attack as a large-scale optimization problem. To efficiently solve the optimization problem, a three-stage process \textit{Ask, Attend, Attack}, called \textit{AAA}, is proposed to coordinate with the solver. \textit{Ask} guides attackers to create target texts that satisfy the specific semantics. \textit{Attend} identifies the crucial regions of the image for attacking, thus reducing the search space for the subsequent \textit{Attack}. \textit{Attack} uses an evolutionary algorithm to attack the crucial regions, where the attacks are semantically related to the target texts of \textit{Ask}, thus achieving targeted attacks without semantic loss. Experimental results on transformer-based and CNN+RNN-based image-to-text models confirmed the effectiveness of our proposed \textit{AAA}. | Ask, Attend, Attack: An Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models | [
"Qingyuan Zeng",
"Zhenzhong Wang",
"Yiu-ming Cheung",
"Min Jiang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9uKeqtIoGZ | @inproceedings{
russo2024online,
title={Online Learning with Sublinear Best-Action Queries},
author={Matteo Russo and Andrea Celli and Riccardo Colini Baldeschi and Federico Fusco and Daniel Haimovich and Dima Karamshuk and Stefano Leonardi and Niek Tax},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9uKeqtIoGZ}
} | In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred. Following the recent line of research on algorithms endowed with additional predictive features, we revisit this problem by allowing the decision maker to acquire additional information on the actions to be selected. In particular, we study the power of \emph{best-action queries}, which reveal beforehand the identity of the best action at a given time step. In practice, predictive features may be expensive, so we allow the decision maker to issue at most $k$ such queries.
We establish tight bounds on the performance any algorithm can achieve when given access to $k$ best-action queries for different types of feedback models. In particular, we prove that in the full feedback model, $k$ queries are enough to achieve an optimal regret of $\Theta(\min\{\sqrt T, \frac{T}{k}\})$. This finding highlights the significant multiplicative advantage in the regret rate achievable with even a modest (sublinear) number $k \in \Omega(\sqrt{T})$ of queries.
Additionally, we study the challenging setting in which the only available feedback is obtained during the time steps corresponding to the $k$ best-action queries. There, we provide a tight regret rate of $\Theta(\min\{\frac{T}{\sqrt k},\frac{T^2}{k^2}\})$, which improves over the standard $\Theta(\frac{T}{\sqrt k})$ regret rate for label efficient prediction for $k \in \Omega(T^{2/3})$. | Online Learning with Sublinear Best-Action Queries | [
"Matteo Russo",
"Andrea Celli",
"Riccardo Colini Baldeschi",
"Federico Fusco",
"Daniel Haimovich",
"Dima Karamshuk",
"Stefano Leonardi",
"Niek Tax"
] | NeurIPS.cc/2024/Conference | 2407.16355 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9sP4oejtjB | @inproceedings{
jha2024disentangling,
title={Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems},
author={Aditi Jha and Diksha Gupta and Carlos D Brody and Jonathan W. Pillow},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9sP4oejtjB}
} | Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and to predict the effects of cell-specific perturbations on neural activity or behavior. To overcome these limitations, we introduce the `"cell-type dynamical systems" (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations.
The CTDS model defines separate latents for both cell types, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that the animal's choice can be decoded from both E and I latents and thus is not restricted to a single cell-class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the experimentally observed effects of different perturbations on behavior, whereas a standard LDS model---which does not differentiate between cell types---did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior. | Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems | [
"Aditi Jha",
"Diksha Gupta",
"Carlos D Brody",
"Jonathan W. Pillow"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=9m87e9Keq1 | @inproceedings{
setlur2024rl,
title={{RL} on Incorrect Synthetic Data Scales the Efficiency of {LLM} Math Reasoning by Eight-Fold},
author={Amrith Setlur and Saurabh Garg and Xinyang Geng and Naman Garg and Virginia Smith and Aviral Kumar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9m87e9Keq1}
} | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the same synthetic problems. At the same time, training on model-generated positives can amplify various spurious correlations, resulting in flat or even inverse scaling trends as the amount of data increases. Surprisingly, we find that several of these issues can be addressed if we also utilize negative responses, i.e., model-generated responses that are deemed incorrect by a final answer verifier. Crucially, these negatives must be constructed such that the training can appropriately recover the utility or advantage of each intermediate step in the negative response. With this per-step scheme, we are able to attain consistent gains over only positive data, attaining performance similar to amplifying the amount of synthetic data by $\mathbf{8 \times}$. We show that training on per-step negatives can help to unlearn spurious correlations in the positive data, and is equivalent to advantage-weighted reinforcement learning (RL), implying that it inherits robustness benefits of RL over imitating positive data alone. | RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold | [
"Amrith Setlur",
"Saurabh Garg",
"Xinyang Geng",
"Naman Garg",
"Virginia Smith",
"Aviral Kumar"
] | NeurIPS.cc/2024/Conference | 2406.14532 | [
"https://github.com/ars22/scaling-LLM-math-synthetic-data"
] | https://huggingface.co/papers/2406.14532 | 0 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=9lGJrkqJUw | @inproceedings{
kirchmeyer2024scorebased,
title={Score-based 3D molecule generation with neural fields},
author={Matthieu Kirchmeyer and Pedro O. Pinheiro and Saeed Saremi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9lGJrkqJUw}
} | We introduce a new functional representation for 3D molecules based on their continuous atomic density fields. Using this representation, we propose a new model based on neural empirical Bayes for unconditional 3D molecule generation in the continuous space using neural fields. Our model, FuncMol, encodes molecular fields into latent codes using a conditional neural field, samples noisy codes from a Gaussian-smoothed distribution with Langevin MCMC, denoises these samples in a single step and finally decodes them into molecular fields. FuncMol performs all-atom generation of 3D molecules without assumptions on the molecular structure and scales well with the size of molecules, unlike most existing approaches. Our method achieves competitive results on drug-like molecules and easily scales to macro-cyclic peptides, with at least one order of magnitude faster sampling. The code is available at https://github.com/prescient-design/funcmol. | Score-based 3D molecule generation with neural fields | [
"Matthieu Kirchmeyer",
"Pedro O. Pinheiro",
"Saeed Saremi"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9jgODkdH0F | @inproceedings{
bai2024connectivity,
title={Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion},
author={Zhiwei Bai and Jiajie Zhao and Yaoyu Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9jgODkdH0F}
} | Matrix factorization models have been extensively studied as a valuable test-bed for understanding the implicit biases of overparameterized models. Although both low nuclear norm and low rank regularization have been studied for these models, a unified understanding of when, how, and why they achieve different implicit regularization effects remains elusive. In this work, we systematically investigate the implicit regularization of matrix factorization for solving matrix completion problems. We empirically discover that the connectivity of observed data plays a key role in the implicit bias, with a transition from low nuclear norm to low rank as data shifts from disconnected to connected with increased observations. We identify a hierarchy of intrinsic invariant manifolds in the loss landscape that guide the training trajectory to evolve from low-rank to higher-rank solutions. Based on this finding, we theoretically characterize the training trajectory as following the hierarchical invariant manifold traversal process, generalizing the characterization of Li et al.(2020) to include the disconnected case. Furthermore, we establish conditions that guarantee minimum nuclear norm, closely aligning with our experimental findings, and we provide a dynamics characterization condition for ensuring minimum rank. Our work reveals the intricate interplay between data connectivity, training dynamics, and implicit regularization in matrix factorization models. | Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion | [
"Zhiwei Bai",
"Jiajie Zhao",
"Yaoyu Zhang"
] | NeurIPS.cc/2024/Conference | 2405.13721 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9hKN99RNdR | @inproceedings{
duan2024exploring,
title={Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning},
author={Yuanlin Duan and Guofeng Cui and He Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9hKN99RNdR}
} | Exploring unknown environments efficiently is a fundamental challenge in unsupervised goal-conditioned reinforcement learning. While selecting exploratory goals at the frontier of previously explored states is an effective strategy, the policy during training may still have limited capability of reaching rare goals on the frontier, resulting in reduced exploratory behavior. We propose "Cluster Edge Exploration" (CE$^2$), a new goal-directed exploration algorithm that when choosing goals in sparsely explored areas of the state space gives priority to goal states that remain accessible to the agent. The key idea is clustering to group states that are easily reachable from one another by the current policy under training in a latent space, and traversing to states holding significant exploration potential on the boundary of these clusters before doing exploratory behavior. In challenging robotics environments including navigating a maze with a multi-legged ant robot, manipulating objects with a robot arm on a cluttered tabletop, and rotating objects in the palm of an anthropomorphic robotic hand, CE$^2$ demonstrates superior efficiency in exploration compared to baseline methods and ablations. | Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning | [
"Yuanlin Duan",
"Guofeng Cui",
"He Zhu"
] | NeurIPS.cc/2024/Conference | 2411.01396 | [
"https://github.com/RU-Automated-Reasoning-Group/CE2"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9hCn01VAdC | @inproceedings{
luo2024knowledgeempowered,
title={Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series},
author={Yicheng Luo and Zhen Liu and Linghao Wang and Binquan Wu and Junhao Zheng and Qianli Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9hCn01VAdC}
} | Irregularly Sampled Medical Time Series (ISMTS) are commonly found in the healthcare domain, where different variables exhibit unique temporal patterns while interrelated. However, many existing methods fail to efficiently consider the differences and correlations among medical variables together, leading to inadequate capture of fine-grained features at the variable level in ISMTS. We propose Knowledge-Empowered Dynamic Graph Network (KEDGN), a graph neural network empowered by variables' textual medical knowledge, aiming to model variable-specific temporal dependencies and inter-variable dependencies in ISMTS. Specifically, we leverage a pre-trained language model to extract semantic representations for each variable from their textual descriptions of medical properties, forming an overall semantic view among variables from a medical perspective. Based on this, we allocate variable-specific parameter spaces to capture variable-specific temporal patterns and generate a complete variable graph to measure medical correlations among variables. Additionally, we employ a density-aware mechanism to dynamically adjust the variable graph at different timestamps, adapting to the time-varying correlations among variables in ISMTS. The variable-specific parameter spaces and dynamic graphs are injected into the graph convolutional recurrent network to capture intra-variable and inter-variable dependencies in ISMTS together. Experiment results on four healthcare datasets demonstrate that KEDGN significantly outperforms existing methods. | Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series | [
"Yicheng Luo",
"Zhen Liu",
"Linghao Wang",
"Binquan Wu",
"Junhao Zheng",
"Qianli Ma"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9f5tOXKoMC | @inproceedings{
xu2024a,
title={A Bayesian Approach to Data Point Selection},
author={Xinnuo Xu and Minyoung Kim and Royson Lee and Brais Martinez and Timothy Hospedales},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9f5tOXKoMC}
} | Data point selection (DPS) is becoming a critical topic in deep learning due to the ease of acquiring uncurated training data compared to the difficulty of obtaining curated or processed data.
Existing approaches to DPS are predominantly based on a bi-level optimisation (BLO) formulation, which is demanding in terms of memory and computation, and exhibits some theoretical defects regarding minibatches.
Thus, we propose a novel Bayesian approach to DPS. We view the DPS problem as posterior inference in a novel Bayesian model where the posterior distributions of the instance-wise weights and the main neural network parameters are inferred under a reasonable prior and likelihood model.
We employ stochastic gradient Langevin MCMC sampling to learn the main network and instance-wise weights jointly, ensuring convergence even with minibatches. Our update equation is comparable to the widely used SGD and much more efficient than existing BLO-based methods. Through controlled experiments in both the vision and language domains, we present the proof-of-concept. Additionally, we demonstrate that our method scales effectively to large language models and facilitates automated per-task optimization for instruction fine-tuning datasets. | A Bayesian Approach to Data Point Selection | [
"Xinnuo Xu",
"Minyoung Kim",
"Royson Lee",
"Brais Martinez",
"Timothy Hospedales"
] | NeurIPS.cc/2024/Conference | 2411.03768 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9cFyqhjEHC | @inproceedings{
bar-shalom2024a,
title={A Flexible, Equivariant Framework for Subgraph {GNN}s via Graph Products and Graph Coarsening},
author={Guy Bar-Shalom and Yam Eitan and Fabrizio Frasca and Haggai Maron},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9cFyqhjEHC}
} | Subgraph GNNs enhance message-passing GNNs expressivity by representing graphs as sets of subgraphs, demonstrating impressive performance across various tasks. However, their scalability is hindered by the need to process large numbers of subgraphs. While previous approaches attempted to generate smaller subsets of subgraphs through random or learnable sampling, these methods often yielded suboptimal selections or were limited to small subset sizes, ultimately compromising their effectiveness. This paper introduces a new Subgraph GNN framework to address these issues.
Our approach diverges from most previous methods by associating subgraphs with node clusters rather than with individual nodes. We show that the resulting collection of subgraphs can be viewed as the product of coarsened and original graphs, unveiling a new connectivity structure on which we perform generalized message passing.
Crucially, controlling the coarsening function enables meaningful selection of any number of subgraphs. In addition, we reveal novel permutation symmetries in the resulting node feature tensor, characterize associated linear equivariant layers, and integrate them into our Subgraph GNN. We also introduce novel node marking strategies and provide a theoretical analysis of their expressive power and other key aspects of our approach. Extensive experiments on multiple graph learning benchmarks demonstrate that our method is significantly more flexible than previous approaches, as it can seamlessly handle any number of subgraphs, while consistently outperforming baseline approaches.
Our code is available at https://github.com/BarSGuy/Efficient-Subgraph-GNNs. | A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening | [
"Guy Bar-Shalom",
"Yam Eitan",
"Fabrizio Frasca",
"Haggai Maron"
] | NeurIPS.cc/2024/Conference | 2406.09291 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9c3IiAWeiN | @inproceedings{
gao2024ipmlstm,
title={{IPM}-{LSTM}: A Learning-Based Interior Point Method for Solving Nonlinear Programs},
author={Xi Gao and Jinxin Xiong and Akang Wang and Qihong Duan and Jiang Xue and Qingjiang Shi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9c3IiAWeiN}
} | Solving constrained nonlinear programs (NLPs) is of great importance in various domains such as power systems, robotics, and wireless communication networks. One widely used approach for addressing NLPs is the interior point method (IPM). The most computationally expensive procedure in IPMs is to solve systems of linear equations via matrix factorization. Recently, machine learning techniques have been adopted to expedite classic optimization algorithms. In this work, we propose using Long Short-Term Memory (LSTM) neural networks to approximate the solution of linear systems and integrate this approximating step into an IPM. The resulting approximate NLP solution is then utilized to warm-start an interior point solver. Experiments on various types of NLPs, including Quadratic Programs and Quadratically Constrained Quadratic Programs, show that our approach can significantly accelerate NLP solving, reducing iterations by up to 60% and solution time by up to 70% compared to the default solver. | IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs | [
"Xi Gao",
"Jinxin Xiong",
"Akang Wang",
"Qihong Duan",
"Jiang Xue",
"Qingjiang Shi"
] | NeurIPS.cc/2024/Conference | 2410.15731 | [
"https://github.com/NetSysOpt/IPM-LSTM"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9bu627mTfs | @inproceedings{
yu2024context,
title={Context and Geometry Aware Voxel Transformer for Semantic Scene Completion},
author={Zhu Yu and Runmin Zhang and Jiacheng Ying and Junchen Yu and Xiaohai Hu and Lun Luo and Si-Yuan Cao and Hui-liang Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9bu627mTfs}
} | Vision-based Semantic Scene Completion (SSC) has gained much attention due to its widespread applications in various 3D perception tasks. Existing sparse-to-dense approaches typically employ shared context-independent queries across various input images, which fails to capture distinctions among them as the focal regions of different inputs vary and may result in undirected feature aggregation of cross-attention. Additionally, the absence of depth information may lead to points projected onto the image plane sharing the same 2D position or similar sampling points in the feature map, resulting in depth ambiguity. In this paper, we present a novel context and geometry aware voxel transformer. It utilizes a context aware query generator to initialize context-dependent queries tailored to individual input images, effectively capturing their unique characteristics and aggregating information within the region of interest. Furthermore, it extend deformable cross-attention from 2D to 3D pixel space, enabling the differentiation of points with similar image coordinates based on their depth coordinates. Building upon this module, we introduce a neural network named CGFormer to achieve semantic scene completion. Simultaneously, CGFormer leverages multiple 3D representations (i.e., voxel and TPV) to boost the semantic and geometric representation abilities of the transformed 3D volume from both local and global perspectives. Experimental results demonstrate that CGFormer achieves state-of-the-art performance on the SemanticKITTI and SSCBench-KITTI-360 benchmarks, attaining a mIoU of 16.87 and 20.05, as well as an IoU of 45.99 and 48.07, respectively. Remarkably, CGFormer even outperforms approaches employing temporal images as inputs or much larger image backbone networks. | Context and Geometry Aware Voxel Transformer for Semantic Scene Completion | [
"Zhu Yu",
"Runmin Zhang",
"Jiacheng Ying",
"Junchen Yu",
"Xiaohai Hu",
"Lun Luo",
"Si-Yuan Cao",
"Hui-liang Shen"
] | NeurIPS.cc/2024/Conference | 2405.13675 | [
"https://github.com/pkqbajng/cgformer"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=9Y8zUO11EQ | @inproceedings{
m{\"u}ndler2024swtbench,
title={{SWT}-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents},
author={Niels M{\"u}ndler and Mark Niklas Mueller and Jingxuan He and Martin Vechev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9Y8zUO11EQ}
} | Rigorous software testing is crucial for developing and maintaining high-quality code, making automated test generation a promising avenue for both improving software quality and boosting the effectiveness of code generation methods. However, while code generation with Large Language Models (LLMs) is an extraordinarily active research area, test generation remains relatively unexplored. We address this gap and investigate the capability of LLM-based Code Agents to formalize user issues into test cases. To this end, we propose a novel benchmark based on popular GitHub repositories, containing real-world issues, ground-truth bug-fixes, and golden tests. We find that LLMs generally perform surprisingly well at generating relevant test cases, with Code Agents designed for code repair exceeding the performance of systems designed specifically for test generation. Further, as test generation is a similar but more structured task than code generation, it allows for a more fine-grained analysis using issue reproduction rate and coverage changes, providing a dual metric for analyzing systems designed for code repair. Finally, we find that generated tests are an effective filter for proposed code fixes, doubling the precision of SWE-Agent. We release all data and code at https://github.com/logic-star-ai/SWT-Bench. | SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents | [
"Niels Mündler",
"Mark Niklas Mueller",
"Jingxuan He",
"Martin Vechev"
] | NeurIPS.cc/2024/Conference | 2406.12952 | [
"https://github.com/logic-star-ai/swt-bench"
] | https://huggingface.co/papers/2406.12952 | 0 | 0 | 0 | 4 | [] | [
"nmuendler/SWT-bench_Lite_bm25_27k_zsb",
"nmuendler/SWT-bench_Lite_bm25_27k_zsp",
"nmuendler/SWT-bench_bm25_27k_zsb",
"nmuendler/SWT-bench_bm25_27k_zsp"
] | [] | [] | [
"nmuendler/SWT-bench_Lite_bm25_27k_zsb",
"nmuendler/SWT-bench_Lite_bm25_27k_zsp",
"nmuendler/SWT-bench_bm25_27k_zsb",
"nmuendler/SWT-bench_bm25_27k_zsp"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=9XDYEEBRV6 | @inproceedings{
moradi2024coded,
title={Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework},
author={Parsa Moradi and Behrooz Tahmasebi and Mohammad Ali Maddah-Ali},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9XDYEEBRV6}
} | Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker node processes a combination of the data, rather than the raw data itself. The final result then is decoded from the collective outputs of the worker nodes. However, there is a significant gap between current coded computing approaches and the broader landscape of general distributed computing, particularly when it comes to machine learning workloads. To bridge this gap, we propose a novel foundation for coded computing, integrating the principles of learning theory, and developing a framework that seamlessly adapts with machine learning applications.
In this framework, the objective is to find the encoder and decoder functions that minimize the loss function, defined as the mean squared error between the estimated and true values. Facilitating the search for the optimum decoding and functions, we show that the loss function can be upper-bounded by the summation of two terms: the generalization error of the decoding function and the training error of the encoding function.
Focusing on
the second-order Sobolev space, we then derive the optimal encoder and decoder. We show that in the proposed solution, the mean squared error of the estimation decays with the rate of $\mathcal{O}(S^3 N^{-3})$ and $\mathcal{O}(S^{\frac{8}{5}}N^{\frac{-3}{5}})$ in noiseless and noisy computation settings, respectively, where $N$ is the number of worker nodes with at most $S$ slow servers (stragglers). Finally, we evaluate the proposed scheme on inference tasks for various machine learning models and demonstrate that the proposed framework outperforms the state-of-the-art in terms of accuracy and rate of convergence. | Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework | [
"Parsa Moradi",
"Behrooz Tahmasebi",
"Mohammad Ali Maddah-Ali"
] | NeurIPS.cc/2024/Conference | 2406.00300 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9VnevS2YoR | @inproceedings{
hu2024vision,
title={Vision Mamba Mender},
author={Jiacong Hu and Anda Cao and Zunlei Feng and Shengxuming Zhang and Yi Wang and Lingxiang Jia and Mingli Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9VnevS2YoR}
} | Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. | Vision Mamba Mender | [
"Jiacong Hu",
"Anda Cao",
"Zunlei Feng",
"Shengxuming Zhang",
"Yi Wang",
"Lingxiang Jia",
"Mingli Song"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9VbGjXLzig | @inproceedings{
udandarao2024no,
title={No ''Zero-Shot'' Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance},
author={Vishaal Udandarao and Ameya Prabhu and Adhiraj Ghosh and Yash Sharma and Philip Torr and Adel Bibi and Samuel Albanie and Matthias Bethge},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9VbGjXLzig}
} | Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during "zero-shot" evaluation. In this work, we ask: How is the performance of multimodal models on downstream concepts influenced by the frequency of these concepts in their pretraining datasets?
We comprehensively investigate this question across 34 models and 5 standard pretraining datasets (CC-3M, CC-12M, YFCC-15M, LAION-400M, LAION-Aesthetics), generating over 300GB of data artifacts. We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance, following a sample inefficient log-linear scaling trend. This trend persists even when controlling for sample-level similarity between pretraining and downstream datasets, and testing on purely synthetic data distributions. Furthermore, upon benchmarking models on long-tailed data sampled based on our analysis, we demonstrate that multimodal models across the board perform poorly. We contribute this long-tail test set as the Let it Wag! benchmark to further research in this direction. Taken together, our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training data and compute paradigms remains to be found. | No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance | [
"Vishaal Udandarao",
"Ameya Prabhu",
"Adhiraj Ghosh",
"Yash Sharma",
"Philip Torr",
"Adel Bibi",
"Samuel Albanie",
"Matthias Bethge"
] | NeurIPS.cc/2024/Conference | 2404.04125 | [
"https://github.com/bethgelab/frequency_determines_performance"
] | https://huggingface.co/papers/2404.04125 | 4 | 27 | 1 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=9U0nLnNMJ7 | @inproceedings{
muralidharan2024compact,
title={Compact Language Models via Pruning and Knowledge Distillation},
author={Saurav Muralidharan and Sharath Turuvekere Sreenivas and Raviraj Bhuminand Joshi and Marcin Chochowski and Mostofa Patwary and Mohammad Shoeybi and Bryan Catanzaro and Jan Kautz and Pavlo Molchanov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9U0nLnNMJ7}
} | Large language models (LLMs) targeting different deployment scales and sizes are currently produced by training each variant from scratch; this is extremely compute-intensive. In this paper, we investigate if pruning an existing LLM and then re-training it with a fraction <3% of the original training data can be a suitable alternative to repeated, full retraining. To this end, we develop a set of practical and effective **compression best practices** for LLMs that combine depth, width, attention and MLP pruning with knowledge distillation-based retraining; we arrive at these best practices through a detailed empirical exploration of pruning strategies for each axis, methods to combine axes, distillation strategies, and search techniques for arriving at optimal compressed architectures. We use this guide to compress the Nemotron-4 family of LLMs by a factor of 2-4x, and compare their performance to similarly-sized models on a variety of language modeling tasks. On these tasks, we perform better than Nemotron-3 8B and LLaMa2 7B using **up to 40x** fewer training tokens}, on par with Mistral 7B and Gemma 7B using **up to 85x fewer tokens** and slightly worse than LLaMa3 8B using **up to 159x fewer tokens**. Our models also compare favorably to state-of-the-art compression techniques from the literature. | Compact Language Models via Pruning and Knowledge Distillation | [
"Saurav Muralidharan",
"Sharath Turuvekere Sreenivas",
"Raviraj Bhuminand Joshi",
"Marcin Chochowski",
"Mostofa Patwary",
"Mohammad Shoeybi",
"Bryan Catanzaro",
"Jan Kautz",
"Pavlo Molchanov"
] | NeurIPS.cc/2024/Conference | 2407.14679 | [
"https://github.com/nvlabs/minitron"
] | https://huggingface.co/papers/2407.14679 | 3 | 38 | 2 | 9 | [
"nvidia/Llama-3.1-Minitron-4B-Width-Base",
"nvidia/Mistral-NeMo-Minitron-8B-Base",
"nvidia/Nemotron-Mini-4B-Instruct",
"nvidia/Minitron-4B-Base",
"nvidia/Minitron-8B-Base",
"nvidia/Mistral-NeMo-Minitron-8B-Instruct",
"nvidia/Llama-3.1-Minitron-4B-Depth-Base",
"QuantFactory/Mistral-NeMo-Minitron-8B-Base-GGUF",
"QuantFactory/Mistral-NeMo-Minitron-8B-Instruct-GGUF",
"mgoin/Minitron-8B-Base-FP8",
"ThomasBaruzier/Llama-3.1-Minitron-4B-Width-Base-GGUF",
"abiks/Nemotron-Mini-4B-Instruct-GGUF-Q8",
"mgoin/Nemotron-4-340B-Base-hf-FP8",
"QuantFactory/Nemotron-Mini-4B-Instruct-GGUF",
"mgoin/Nemotron-4-340B-Base-hf",
"IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml",
"QuantFactory/Llama-3.1-Minitron-4B-Depth-Base-GGUF",
"RichardErkhov/nvidia_-_Mistral-NeMo-Minitron-8B-Base-gguf",
"QuantFactory/Llama-3.1-Minitron-4B-Width-Base-GGUF",
"QuantFactory/Minitron-4B-Base-GGUF",
"QuantFactory/Minitron-8B-Base-GGUF",
"mylesgoose/Llama-3.1-Minitron-4B-Width-Base",
"mav23/Mistral-NeMo-Minitron-8B-Instruct-GGUF",
"denkijin/Llama-3.1-Minitron-4B-Width-Base",
"TitanML/Mistral-NeMo-Minitron-8B-Base",
"lucyknada/nvidia_Mistral-NeMo-Minitron-8B-Instruct-v3-exl2",
"mav23/Mistral-NeMo-Minitron-8B-Base-GGUF"
] | [] | [
"nvidia/minitron",
"Tonic/Nemo-Mistral-Minitron",
"Tonic/Minitron",
"datacipen/eventia",
"sikkha/nvidia-Mistral-NeMo-Minitron-8B-Base",
"DexterSptizu/nvidia-Minitron-4B-Base",
"Roberta2024/Nvidia_RAG_pdf",
"Spencer525/PDF8BRAG",
"YU-XI/Nvidia_RAG_PDF_V1_20240826",
"Sam1010/nvidia-Mistral-NeMo-Minitron-8B-Base",
"Spencer525/Tea8BPDf",
"sidcww/RAGpdf_Nvidia",
"Roberta2024/Multidata0828",
"sidcww/0828Multidata",
"Spencer525/mul8BgemPF",
"JERNGOC/Multimodal_R-A-G_NA_GE_GR-1",
"Roberta2024/optimizaitonMultimodaldata",
"Spencer525/TeMM8Bpdf",
"sidcww/RAFX-1",
"JERNGOC/Multimodal_R-A-G_NA_GE_GR-2",
"Spencer525/nou27MM",
"CyberScripter/Mach-ai",
"epassos/nvidia-Mistral-NeMo-Minitron-8B-Base",
"JaineDev/Jainespace3",
"Rooobert/Nvidia_RAG_pdf_R"
] | [
"nvidia/Llama-3.1-Minitron-4B-Width-Base",
"nvidia/Mistral-NeMo-Minitron-8B-Base",
"nvidia/Nemotron-Mini-4B-Instruct",
"nvidia/Minitron-4B-Base",
"nvidia/Minitron-8B-Base",
"nvidia/Mistral-NeMo-Minitron-8B-Instruct",
"nvidia/Llama-3.1-Minitron-4B-Depth-Base",
"QuantFactory/Mistral-NeMo-Minitron-8B-Base-GGUF",
"QuantFactory/Mistral-NeMo-Minitron-8B-Instruct-GGUF",
"mgoin/Minitron-8B-Base-FP8",
"ThomasBaruzier/Llama-3.1-Minitron-4B-Width-Base-GGUF",
"abiks/Nemotron-Mini-4B-Instruct-GGUF-Q8",
"mgoin/Nemotron-4-340B-Base-hf-FP8",
"QuantFactory/Nemotron-Mini-4B-Instruct-GGUF",
"mgoin/Nemotron-4-340B-Base-hf",
"IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml",
"QuantFactory/Llama-3.1-Minitron-4B-Depth-Base-GGUF",
"RichardErkhov/nvidia_-_Mistral-NeMo-Minitron-8B-Base-gguf",
"QuantFactory/Llama-3.1-Minitron-4B-Width-Base-GGUF",
"QuantFactory/Minitron-4B-Base-GGUF",
"QuantFactory/Minitron-8B-Base-GGUF",
"mylesgoose/Llama-3.1-Minitron-4B-Width-Base",
"mav23/Mistral-NeMo-Minitron-8B-Instruct-GGUF",
"denkijin/Llama-3.1-Minitron-4B-Width-Base",
"TitanML/Mistral-NeMo-Minitron-8B-Base",
"lucyknada/nvidia_Mistral-NeMo-Minitron-8B-Instruct-v3-exl2",
"mav23/Mistral-NeMo-Minitron-8B-Base-GGUF"
] | [] | [
"nvidia/minitron",
"Tonic/Nemo-Mistral-Minitron",
"Tonic/Minitron",
"datacipen/eventia",
"sikkha/nvidia-Mistral-NeMo-Minitron-8B-Base",
"DexterSptizu/nvidia-Minitron-4B-Base",
"Roberta2024/Nvidia_RAG_pdf",
"Spencer525/PDF8BRAG",
"YU-XI/Nvidia_RAG_PDF_V1_20240826",
"Sam1010/nvidia-Mistral-NeMo-Minitron-8B-Base",
"Spencer525/Tea8BPDf",
"sidcww/RAGpdf_Nvidia",
"Roberta2024/Multidata0828",
"sidcww/0828Multidata",
"Spencer525/mul8BgemPF",
"JERNGOC/Multimodal_R-A-G_NA_GE_GR-1",
"Roberta2024/optimizaitonMultimodaldata",
"Spencer525/TeMM8Bpdf",
"sidcww/RAFX-1",
"JERNGOC/Multimodal_R-A-G_NA_GE_GR-2",
"Spencer525/nou27MM",
"CyberScripter/Mach-ai",
"epassos/nvidia-Mistral-NeMo-Minitron-8B-Base",
"JaineDev/Jainespace3",
"Rooobert/Nvidia_RAG_pdf_R"
] | 1 | poster |
null | https://openreview.net/forum?id=9SpWvX9ykp | @inproceedings{
dainese2024generating,
title={Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search},
author={Nicola Dainese and Matteo Merler and Minttu Alakuijala and Pekka Marttinen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9SpWvX9ykp}
} | In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL). Calling code instead of LLMs for planning has potential to be more precise, reliable, interpretable, and extremely efficient.
However, writing appropriate Code World Models requires the ability to understand complex instructions, to generate exact code with non-trivial logic and to self-debug a long program with feedback from unit tests and environment trajectories. To address these challenges, we propose Generate, Improve and Fix with Monte Carlo Tree Search (GIF-MCTS), a new code generation strategy for LLMs. To test our approach in an offline RL setting, we introduce the Code World Models Benchmark (CWMB), a suite of program synthesis and planning tasks comprised of 18 diverse RL environments paired with corresponding textual descriptions and curated trajectories. GIF-MCTS surpasses all baselines on the CWMB and two other benchmarks, and we show that the Code World Models synthesized with it can be successfully used for planning, resulting in model-based RL agents with greatly improved sample efficiency and inference speed. | Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search | [
"Nicola Dainese",
"Matteo Merler",
"Minttu Alakuijala",
"Pekka Marttinen"
] | NeurIPS.cc/2024/Conference | 2405.15383 | [
"https://github.com/nicoladainese96/code-world-models"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9SghPrjYU1 | @inproceedings{
liu2024minimax,
title={Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning},
author={Zhishuai Liu and Pan Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9SghPrjYU1}
} | Distributionally robust offline reinforcement learning (RL), which seeks robust policy training against environment perturbation by modeling dynamics uncertainty, calls for function approximations when facing large state-action spaces. However, the consideration of dynamics uncertainty introduces essential nonlinearity and computational burden, posing unique challenges for analyzing and practically employing function approximation. Focusing on a basic setting where the nominal model and perturbed models are linearly parameterized, we propose minimax optimal and computationally efficient algorithms realizing function approximation and initiate the study on instance-dependent suboptimality analysis in the context of robust offline RL. Our results uncover that function approximation in robust offline RL is essentially distinct from and probably harder than that in standard offline RL. Our algorithms and theoretical results crucially depend on a novel function approximation mechanism incorporating variance information, a new procedure of suboptimality and estimation uncertainty decomposition, a quantification of the robust value function shrinkage, and a meticulously designed family of hard instances, which might be of independent interest. | Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning | [
"Zhishuai Liu",
"Pan Xu"
] | NeurIPS.cc/2024/Conference | 2403.09621 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9Q9UiAyV40 | @inproceedings{
liu2024mspe,
title={{MSPE}: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution},
author={Wenzhuo Liu and Fei Zhu and Shijie Ma and Cheng-Lin Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9Q9UiAyV40}
} | Although Vision Transformers (ViTs) have recently advanced computer vision tasks significantly, an important real-world problem was overlooked: adapting to variable input resolutions. Typically, images are resized to a fixed resolution, such as 224x224, for efficiency during training and inference. However, uniform input size conflicts with real-world scenarios where images naturally vary in resolution. Modifying the preset resolution of a model may severely degrade the performance. In this work, we propose to enhance the model adaptability to resolution variation by optimizing the patch embedding. The proposed method, called Multi-Scale Patch Embedding (MSPE), substitutes the standard patch embedding with multiple variable-sized patch kernels and selects the best parameters for different resolutions, eliminating the need to resize the original image. Our method does not require high-cost training or modifications to other parts, making it easy to apply to most ViT models. Experiments in image classification, segmentation, and detection tasks demonstrate the effectiveness of MSPE, yielding superior performance on low-resolution inputs and performing comparably on high-resolution inputs with existing methods. | MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution | [
"Wenzhuo Liu",
"Fei Zhu",
"Shijie Ma",
"Cheng-Lin Liu"
] | NeurIPS.cc/2024/Conference | 2405.18240 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9Pa6cCB3gL | @inproceedings{
xi2024umb,
title={{UMB}: Understanding Model Behavior for Open-World Object Detection},
author={Xing Xi and Yangyang Huang and Zhijie Zhong and Ronghua Luo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9Pa6cCB3gL}
} | Open-World Object Detection (OWOD) is a challenging task that requires the detector to identify unlabeled objects and continuously demands the detector to learn new knowledge based on existing ones. Existing methods primarily focus on recalling unknown objects, neglecting to explore the reasons behind them. This paper aims to understand the model's behavior in predicting the unknown category. First, we model the text attribute and the positive sample probability, obtaining their empirical probability, which can be seen as the detector's estimation of the likelihood of the target with certain known attributes being predicted as the foreground. Then, we jointly decide whether the current object should be categorized in the unknown category based on the empirical, the in-distribution, and the out-of-distribution probability. Finally, based on the decision-making process, we can infer the similarity of an unknown object to known classes and identify the attribute with the most significant impact on the decision-making process. This additional information can help us understand the behavior of the model's prediction in the unknown class. The evaluation results on the Real-World Object Detection (RWD) benchmark, which consists of five real-world application datasets, show that we surpassed the previous state-of-the-art (SOTA) with an absolute gain of 5.3 mAP for unknown classes, reaching 20.5 mAP. Our code is available at https://github.com/xxyzll/UMB. | UMB: Understanding Model Behavior for Open-World Object Detection | [
"Xing Xi",
"Yangyang Huang",
"Zhijie Zhong",
"Ronghua Luo"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9OHXQybMZB | @inproceedings{
overman2024aligning,
title={Aligning Model Properties via Conformal Risk Control},
author={William Overman and Jacqueline Jil Vallon and Mohsen Bayati},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9OHXQybMZB}
} | AI model alignment is crucial due to inadvertent biases in training data and the underspecified machine learning pipeline, where models with excellent test metrics may not meet end-user requirements. While post-training alignment via human feedback shows promise, these methods are often limited to generative AI settings where humans can interpret and provide feedback on model outputs. In traditional non-generative settings with numerical or categorical outputs, detecting misalignment through single-sample outputs remains challenging, and enforcing alignment during training requires repeating costly training processes.
In this paper we consider an alternative strategy. We propose interpreting model alignment through property testing, defining an aligned model $f$ as one belonging to a subset $\mathcal{P}$ of functions that exhibit specific desired behaviors. We focus on post-processing a pre-trained model $f$ to better align with $\mathcal{P}$ using conformal risk control. Specifically, we develop a general procedure for converting queries for testing a given property $\mathcal{P}$ to a collection of loss functions suitable for use in a conformal risk control algorithm. We prove a probabilistic guarantee that the resulting conformal interval around $f$ contains a function approximately satisfying $\mathcal{P}$. We exhibit applications of our methodology on a collection of supervised learning datasets for (shape-constrained) properties such as monotonicity and concavity. The general procedure is flexible and can be applied to a wide range of desired properties. Finally, we prove that pre-trained models will always require alignment techniques even as model sizes or training data increase, as long as the training data contains even small biases. | Aligning Model Properties via Conformal Risk Control | [
"William Overman",
"Jacqueline Jil Vallon",
"Mohsen Bayati"
] | NeurIPS.cc/2024/Conference | 2406.18777 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9O2sVnEHor | @inproceedings{
paolino2024weisfeiler,
title={Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning},
author={Raffaele Paolino and Sohir Maskey and Pascal Welke and Gitta Kutyniok},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9O2sVnEHor}
} | We introduce $r$-loopy Weisfeiler-Leman ($r$-$\ell$WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, $r$-$\ell$MPNN, that can count cycles up to length $r{+}2$. Most notably, we show that $r$-$\ell$WL can count homomorphisms of cactus graphs. This extends 1-WL, which can only count homomorphisms of trees and, in fact, is incomparable to $k$-WL for any fixed $k$. We empirically validate the expressive and counting power of $r$-$\ell$MPNN on several synthetic datasets and demonstrate the scalability and strong performance on various real-world datasets, particularly on sparse graphs. | Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning | [
"Raffaele Paolino",
"Sohir Maskey",
"Pascal Welke",
"Gitta Kutyniok"
] | NeurIPS.cc/2024/Conference | 2403.13749 | [
"https://github.com/rpaolino/loopy"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=9Nsa4lVZeD | @inproceedings{
zhang2024stability,
title={Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation},
author={Kaibo Zhang and Yunjuan Wang and Raman Arora},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9Nsa4lVZeD}
} | Adversarial training has emerged as a popular approach for training models that are robust to inference-time adversarial attacks. However, our theoretical understanding of why and when it works remains limited. Prior work has offered generalization analysis of adversarial training, but they are either restricted to the Neural Tangent Kernel (NTK) regime or they make restrictive assumptions about data such as (noisy) linear separability or robust realizability. In this work, we study the stability and generalization of adversarial training for two-layer networks **without any data distribution assumptions** and **beyond the NTK regime**. Our findings suggest that for networks with *any given initialization* and *sufficiently large width*, the generalization bound can be effectively controlled via early stopping. We further improve the generalization bound by leveraging smoothing using Moreau’s envelope. | Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation | [
"Kaibo Zhang",
"Yunjuan Wang",
"Raman Arora"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9Jmt1eER9P | @inproceedings{
boyd2024optimization,
title={Optimization Algorithm Design via Electric Circuits},
author={Stephen P. Boyd and Tetiana Parshakova and Ernest K. Ryu and Jaewook J. Suh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9Jmt1eER9P}
} | We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, computer-assisted discretization of the continuous-time dynamics, yielding a provably convergent discrete-time algorithm. Our methodology recovers many classical (distributed) optimization algorithms and enables users to quickly design and explore a wide range of new algorithms with convergence guarantees. | Optimization Algorithm Design via Electric Circuits | [
"Stephen P. Boyd",
"Tetiana Parshakova",
"Ernest K. Ryu",
"Jaewook J. Suh"
] | NeurIPS.cc/2024/Conference | 2411.02573 | [
"https://github.com/cvxgrp/optimization_via_circuits"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=9JFSJitKC0 | @inproceedings{
kim2024spectralrisk,
title={Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees},
author={Dohyeong Kim and Taehyun Cho and Seungyub Han and Hojun Chung and Kyungjae Lee and Songhwai Oh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9JFSJitKC0}
} | The field of risk-constrained reinforcement learning (RCRL) has been developed to effectively reduce the likelihood of worst-case scenarios by explicitly handling risk-measure-based constraints.
However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.
To overcome the difficulties posed by the nonlinearity, we propose a spectral risk measure-constrained RL algorithm, spectral-risk-constrained policy optimization (SRCPO), a bilevel optimization approach that utilizes the duality of spectral risk measures.
In the bilevel optimization structure, the outer problem involves optimizing dual variables derived from the risk measures, while the inner problem involves finding an optimal policy given these dual variables.
The proposed method, to the best of our knowledge, is the first to guarantee convergence to an optimum in the tabular setting.
Furthermore, the proposed method has been evaluated on continuous control tasks and showed the best performance among other RCRL algorithms satisfying the constraints.
Our code is available at https://github.com/rllab-snu/Spectral-Risk-Constrained-RL. | Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees | [
"Dohyeong Kim",
"Taehyun Cho",
"Seungyub Han",
"Hojun Chung",
"Kyungjae Lee",
"Songhwai Oh"
] | NeurIPS.cc/2024/Conference | 2405.18698 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9GhSOp1LYH | @inproceedings{
hu2024leveraging,
title={Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation},
author={Jian Hu and Jiayi Lin and Junchi Yan and Shaogang Gong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9GhSOp1LYH}
} | Promptable segmentation typically requires instance-specific manual prompts to guide the segmentation of each desired object. To minimize such a need, task-generic promptable segmentation has been introduced, which employs a single task-generic prompt to segment various images of different objects in the same task. Current methods use Multimodal Large Language Models (MLLMs) to reason detailed instance-specific prompts from a task-generic prompt for improving segmentation accuracy. The effectiveness of this segmentation heavily depends on the precision of these derived prompts. However, MLLMs often suffer hallucinations during reasoning, resulting in inaccurate prompting. While existing methods focus on eliminating hallucinations to improve a model, we argue that MLLM hallucinations can reveal valuable contextual insights when leveraged correctly, as they represent pre-trained large-scale knowledge beyond individual images. In this paper, we first utilize hallucinations to mine task-related information from images and verify its accuracy to enhance precision of the generated prompts. Specifically, we introduce an iterative \textbf{Pro}mpt-\textbf{Ma}sk \textbf{C}ycle generation framework (ProMaC) with a prompt generator and a mask generator. The prompt generator uses a multi-scale chain of thought prompting, initially leveraging hallucinations to extract extended contextual prompts on a test image. These hallucinations are then minimized to formulate precise instance-specific prompts, directing the mask generator to produce masks that are consistent with task semantics by mask semantic alignment. Iteratively the generated masks induce the prompt generator to focus more on task-relevant image areas and reduce irrelevant hallucinations, resulting jointly in better prompts and masks. Experiments on 5 benchmarks demonstrate the effectiveness of ProMaC. Code is in https://lwpyh.github.io/ProMaC/. | Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation | [
"Jian Hu",
"Jiayi Lin",
"Junchi Yan",
"Shaogang Gong"
] | NeurIPS.cc/2024/Conference | 2408.15205 | [
"https://github.com/lwpyh/ProMaC_code"
] | https://huggingface.co/papers/2408.15205 | 1 | 1 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=9FYat8HPpv | @inproceedings{
chen2024spikereveal,
title={SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams},
author={Kang Chen and Shiyan Chen and Jiyuan Zhang and Baoyue Zhang and Yajing Zheng and Tiejun Huang and Zhaofei Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9FYat8HPpv}
} | Reconstructing a sequence of sharp images from the blurry input is crucial for enhancing our insights into the captured scene and poses a significant challenge due to the limited temporal features embedded in the image. Spike cameras, sampling at rates up to 40,000 Hz, have proven effective in capturing motion features and beneficial for solving this ill-posed problem. Nonetheless, existing methods fall into the supervised learning paradigm, which suffers from notable performance degradation when applied to real-world scenarios that diverge from the synthetic training data domain. To address these challenges, we propose the first self-supervised framework for the task of spike-guided motion deblurring. Our approach begins with the formulation of a spike-guided deblurring model that explores the theoretical relationships among spike streams, blurry images, and their corresponding sharp sequences. We subsequently develop a self-supervised cascaded framework to alleviate the issues of spike noise and spatial-resolution mismatching encountered in the deblurring model. With knowledge distillation and re-blurring loss, we further design a lightweight deblur network to generate high-quality sequences with brightness and texture consistency with the original input. Quantitative and qualitative experiments conducted on our real-world and synthetic datasets with spikes validate the superior generalization of the proposed framework. Our code, data and trained models are available at \url{https://github.com/chenkang455/S-SDM}. | SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams | [
"Kang Chen",
"Shiyan Chen",
"Jiyuan Zhang",
"Baoyue Zhang",
"Yajing Zheng",
"Tiejun Huang",
"Zhaofei Yu"
] | NeurIPS.cc/2024/Conference | 2403.09486 | [
"https://github.com/chenkang455/s-sdm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=9CMOrofB75 | @inproceedings{
wang2024evaluating,
title={Evaluating the design space of diffusion-based generative models},
author={Yuqing Wang and Ye He and Molei Tao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9CMOrofB75}
} | Most existing theoretical investigations of the accuracy of diffusion models, albeit significant, assume the score function has been approximated to a certain accuracy, and then use this a priori bound to control the error of generation. This article instead provides a first quantitative understanding of the whole generation process, i.e., both training and sampling. More precisely, it conducts a non-asymptotic convergence analysis of denoising score matching under gradient descent. In addition, a refined sampling error analysis for variance exploding models is also provided. The combination of these two results yields a full error analysis, which elucidates (again, but this time theoretically) how to design the training and sampling processes for effective generation. For instance, our theory implies a preference toward noise distribution and loss weighting in training that qualitatively agree with the ones used in [Karras et al., 2022]. It also provides perspectives on the choices of time and variance schedules in sampling: when the score is well trained, the design in [Song et al., 2021] is more preferable, but when it is less trained, the design in [Karras et al., 2022] becomes more preferable. | Evaluating the design space of diffusion-based generative models | [
"Yuqing Wang",
"Ye He",
"Molei Tao"
] | NeurIPS.cc/2024/Conference | 2406.12839 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9B6J64eTp4 | @inproceedings{
deng2024articulate,
title={Articulate your Ne{RF}: Unsupervised articulated object modeling via conditional view synthesis},
author={Jianning Deng and Kartic Subr and Hakan Bilen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9B6J64eTp4}
} | We propose a novel unsupervised method to learn pose and part-segmentation of articulated objects with rigid parts.
Given two observations of an object in different articulation states, our method learns the geometry and appearance of object parts by using an implicit model from the first observation, distills the part segmentation and articulation from the second observation while rendering the latter observation.
Additionally, to tackle the complexities in the joint optimization of part segmentation and articulation, we propose a voxel grid based initialization strategy and a decoupled optimization procedure.
Compared to the prior unsupervised work, our model obtains significantly better performance, generalizes to objects with multiple parts while it can be efficiently from few views for the latter observation. | Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis | [
"Jianning Deng",
"Kartic Subr",
"Hakan Bilen"
] | NeurIPS.cc/2024/Conference | 2406.16623 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9B0iOkn3UP | @inproceedings{
yang2024computational,
title={Computational Aspects of Bayesian Persuasion under Approximate Best Response},
author={Kunhe Yang and Hanrui Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9B0iOkn3UP}
} | We study Bayesian persuasion under approximate best response, where the receiver may choose any action that is not too much suboptimal, given their posterior belief upon receiving the signal. We focus on the computational aspects of the problem, aiming to design algorithms that efficiently compute (almost) optimal strategies for the sender. Despite the absence of the revelation principle --- which has been one of the most powerful tools in Bayesian persuasion --- we design polynomial-time exact algorithms for the problem when either the state space or the action space is small, as well as a quasi-polynomial-time approximation scheme (QPTAS) for the general problem. On the negative side, we show there is no polynomial-time exact algorithm for the general problem unless $\mathsf{P} = \mathsf{NP}$. Our results build on several new algorithmic ideas, which might be useful in other principal-agent problems where robustness is desired. | Computational Aspects of Bayesian Persuasion under Approximate Best Response | [
"Kunhe Yang",
"Hanrui Zhang"
] | NeurIPS.cc/2024/Conference | 2402.07426 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=99y2EfLe3B | @inproceedings{
shin2024separate,
title={Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation},
author={Ui-Hyeop Shin and Sangyoun Lee and Taehan Kim and Hyung-Min Park},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=99y2EfLe3B}
} | In speech separation, time-domain approaches have successfully replaced the time-frequency domain with latent sequence feature from a learnable encoder. Conventionally, the feature is separated into speaker-specific ones at the final stage of the network. Instead, we propose a more intuitive strategy that separates features earlier by expanding the feature sequence to the number of speakers as an extra dimension. To achieve this, an asymmetric strategy is presented in which the encoder and decoder are partitioned to perform distinct processing in separation tasks. The encoder analyzes features, and the output of the encoder is split into the number of speakers to be separated. The separated sequences are then reconstructed by the weight-shared decoder, which also performs cross-speaker processing.
Without relying on speaker information, the weight-shared network in the decoder directly learns to discriminate features using a separation objective. In addition, to improve performance, traditional methods have extended the sequence length, leading to the adoption of dual-path models, which handle the much longer sequence effectively by segmenting it into chunks. To address this, we introduce global and local Transformer blocks that can directly handle long sequences more efficiently without chunking and dual-path processing. The experimental results demonstrated that this asymmetric structure is effective and that the combination of proposed global and local Transformer can sufficiently replace the role of inter- and intra-chunk processing in dual-path structure. Finally, the presented model combining both of these achieved state-of-the-art performance with much less computation in various benchmark datasets. | Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation | [
"Ui-Hyeop Shin",
"Sangyoun Lee",
"Taehan Kim",
"Hyung-Min Park"
] | NeurIPS.cc/2024/Conference | 2406.05983 | [
"https://github.com/dmlguq456/SepReformer"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=99rOAM7Jfm | @inproceedings{
r{\"a}is{\"a}2024noiseaware,
title={Noise-Aware Differentially Private Regression via Meta-Learning},
author={Ossi R{\"a}is{\"a} and Stratis Markou and Matthew Ashman and Wessel P Bruinsma and Marlon Tobaben and Antti Honkela and Richard E. Turner},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=99rOAM7Jfm}
} | Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the gold standard for protecting user privacy, standard DP mechanisms typically significantly impair performance. One approach to mitigating this issue is pre-training models on simulated data before DP learning on the private data. In this work we go a step further, using simulated data to train a meta-learning model that combines the Convolutional Conditional Neural Process (ConvCNP) with an improved functional DP mechanism of Hall et al. (2013), yielding the DPConvCNP. DPConvCNP learns from simulated data how to map private data to a DP predictive model in one forward pass, and then provides accurate, well-calibrated predictions. We compare DPConvCNP with a DP Gaussian Process (GP) baseline with carefully tuned hyperparameters. The DPConvCNP outperforms the GP baseline, especially on non-Gaussian data, yet is much faster at test time and requires less tuning. | Noise-Aware Differentially Private Regression via Meta-Learning | [
"Ossi Räisä",
"Stratis Markou",
"Matthew Ashman",
"Wessel P Bruinsma",
"Marlon Tobaben",
"Antti Honkela",
"Richard E. Turner"
] | NeurIPS.cc/2024/Conference | 2406.08569 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=97OvPgmjRN | @inproceedings{
rigaux2024enhancing,
title={Enhancing Chess Reinforcement Learning with Graph Representation},
author={Tomas Rigaux and Hisashi Kashima},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=97OvPgmjRN}
} | Mastering games is a hard task, as games can be extremely complex, and still fundamentally different in structure from one another. While the AlphaZero algorithm has demonstrated an impressive ability to learn the rules and strategy of a large variety of games, ranging from Go and Chess, to Atari games, its reliance on extensive computational resources and rigid Convolutional Neural Network (CNN) architecture limits its adaptability and scalability. A model trained to play on a $19\times 19$ Go board cannot be used to play on a smaller $13\times 13$ board, despite the similarity between the two Go variants.
In this paper, we focus on Chess, and explore using a more generic Graph-based Representation of a game state, rather than a grid-based one, to introduce a more general architecture based on Graph Neural Networks (GNN). We also expand the classical Graph Attention Network (GAT) layer to incorporate edge-features, to naturally provide a generic policy output format.
Our experiments, performed on smaller networks than the initial AlphaZero paper, show that this new architecture outperforms previous architectures with a similar number of parameters, being able to increase playing strength an order of magnitude faster. We also show that the model, when trained on a smaller $5\times 5$ variant of chess, is able to be quickly fine-tuned to play on regular $8\times 8$ chess, suggesting that this approach yields promising generalization abilities.
Our code is available at https://github.com/akulen/AlphaGateau. | Enhancing Chess Reinforcement Learning with Graph Representation | [
"Tomas Rigaux",
"Hisashi Kashima"
] | NeurIPS.cc/2024/Conference | 2410.23753 | [
"https://github.com/akulen/alphagateau"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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