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null | https://openreview.net/forum?id=974ojuN0jU | @inproceedings{
gattiglio2024randnetparareal,
title={RandNet-Parareal: a time-parallel {PDE} solver using Random Neural Networks},
author={Guglielmo Gattiglio and Lyudmila Grigoryeva and Massimiliano Tamborrino},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=974ojuN0jU}
} | Parallel-in-time (PinT) techniques have been proposed to solve systems of time-dependent differential equations by parallelizing the temporal domain. Among them, Parareal computes the solution sequentially using an inaccurate (fast) solver, and then ``corrects'' it using an accurate (slow) integrator that runs in parallel across temporal subintervals. This work introduces RandNet-Parareal, a novel method to learn the discrepancy between the coarse and fine solutions using random neural networks (RandNets). RandNet-Parareal achieves speed gains up to x125 and x22 compared to the fine solver run serially and Parareal, respectively. Beyond theoretical guarantees of RandNets as universal approximators, these models are quick to train, allowing the PinT solution of partial differential equations on a spatial mesh of up to $10^5$ points with minimal overhead, dramatically increasing the scalability of existing PinT approaches. RandNet-Parareal's numerical performance is illustrated on systems of real-world significance, such as the viscous Burgers' equation, the Diffusion-Reaction equation, the two- and three-dimensional Brusselator, and the shallow water equation. | RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks | [
"Guglielmo Gattiglio",
"Lyudmila Grigoryeva",
"Massimiliano Tamborrino"
] | NeurIPS.cc/2024/Conference | 2411.06225 | [
"https://github.com/Parallel-in-Time-Differential-Equations/RandNet-Parareal"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=96gXvFYWSE | @inproceedings{
vishwakarma2024pearls,
title={Pearls from Pebbles: Improved Confidence Functions for Auto-labeling},
author={Harit Vishwakarma and Yi Chen and Sui Jiet Tay and Satya Sai Srinath Namburi GNVV and Frederic Sala and Ramya Korlakai Vinayak},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=96gXvFYWSE}
} | Auto-labeling is an important family of techniques that produce labeled training sets with minimum manual annotation. A prominent variant, threshold-based auto-labeling (TBAL), works by finding thresholds on a model's confidence scores above which it can accurately automatically label unlabeled data. However, many models are known to produce overconfident scores, leading to poor TBAL performance. While a natural idea is to apply off-the-shelf calibration methods to alleviate the overconfidence issue, we show that such methods fall short. Rather than experimenting with ad-hoc choices of confidence functions, we propose a framework for studying the optimal TBAL confidence function. We develop a tractable version of the framework to obtain Colander (Confidence functions for Efficient and Reliable Auto-labeling), a new post-hoc method specifically designed to maximize performance in TBAL systems. We perform an extensive empirical evaluation of Colander and compare it against methods designed for calibration. Colander achieves up to 60% improvement on coverage over the baselines while maintaining error level below 5% and using the same amount of labeled data. | Pearls from Pebbles: Improved Confidence Functions for Auto-labeling | [
"Harit Vishwakarma",
"Yi Chen",
"Sui Jiet Tay",
"Satya Sai Srinath Namburi GNVV",
"Frederic Sala",
"Ramya Korlakai Vinayak"
] | NeurIPS.cc/2024/Conference | 2404.16188 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9622QfVSAb | @inproceedings{
shukor2024implicit,
title={Implicit Multimodal Alignment: On the Generalization of Frozen {LLM}s to Multimodal Inputs},
author={Mustafa Shukor and Matthieu Cord},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=9622QfVSAb}
} | Large Language Models (LLMs) have demonstrated impressive performance on multimodal tasks, without any multimodal finetuning. They are the de facto building block for Large Multimodal Models (LMMs), yet, we still lack a proper understanding of their success. In this work, we expose frozen LLMs to image, video, audio and text inputs and analyse their internal representation with the attempt to understand their generalization beyond textual inputs. Our work provides the following **findings.** Perceptual tokens (1) are easily distinguishable from textual ones inside LLMs, with significantly different representations (e.g. live in different narrow cones), and complete translation to textual tokens does not exists. Yet, (2) both perceptual and textual tokens activate similar LLM weights. Despite their differences, (3) perceptual tokens are implicitly aligned to textual tokens inside LLMs, we call this the implicit multimodal alignment effect (IMA), and argue that this is linked to architectural design, helping LLMs to generalize. This provide more evidence to believe that the generalization of LLMs to multimodal inputs is mainly due to their architecture. These findings lead to several **implications.** This work provides several implications. (1) We find a positive correlation between the implicit alignment score and the task performance, suggesting that this could act as a proxy metric for model evaluation and selection. (2) A negative correlation exists regarding hallucinations (e.g. describing non-existing objects in images), revealing that this problem is mainly due to misalignment between the internal perceptual and textual representations. (3) Perceptual tokens change slightly throughout the model, thus, we propose different approaches to skip computations (e.g. in FFN layers), and significantly reduce the inference cost. (4) Due to the slowly changing embeddings across layers, and the high overlap between textual and multimodal activated weights, we compress LLMs by keeping only 1 subnetwork (called alpha-SubNet) that works well across a wide range of multimodal tasks. The code is available here: https://github.com/mshukor/ima-lmms. | Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs | [
"Mustafa Shukor",
"Matthieu Cord"
] | NeurIPS.cc/2024/Conference | 2405.16700 | [
"https://github.com/mshukor/ima-lmms"
] | https://huggingface.co/papers/2405.16700 | 0 | 0 | 0 | 2 | [
"mshukor/IMA-DePALM"
] | [] | [] | [
"mshukor/IMA-DePALM"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=95VyH4VxN9 | @inproceedings{
zhu2024autonomous,
title={Autonomous Driving with Spiking Neural Networks},
author={Rui-Jie Zhu and Ziqing Wang and Leilani H. Gilpin and Jason Eshraghian},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=95VyH4VxN9}
} | Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability and environmental sustainability. We present Spiking Autonomous Driving (SAD), the first unified Spiking Neural Network (SNN) to address the energy challenges faced by autonomous driving systems through its event-driven and energy-efficient nature. SAD is trained end-to-end and consists of three main modules: perception, which processes inputs from multi-view cameras to construct a spatiotemporal bird's eye view; prediction, which utilizes a novel dual-pathway with spiking neurons to forecast future states; and planning, which generates safe trajectories considering predicted occupancy, traffic rules, and ride comfort. Evaluated on the nuScenes dataset, SAD achieves competitive performance in perception, prediction, and planning tasks, while drawing upon the energy efficiency of SNNs. This work highlights the potential of neuromorphic computing to be applied to energy-efficient autonomous driving, a critical step toward sustainable and safety-critical automotive technology. Our code is available at [https://github.com/ridgerchu/SAD](https://github.com/ridgerchu/SAD). | Autonomous Driving with Spiking Neural Networks | [
"Rui-Jie Zhu",
"Ziqing Wang",
"Leilani H. Gilpin",
"Jason Eshraghian"
] | NeurIPS.cc/2024/Conference | 2405.19687 | [
"https://github.com/ridgerchu/sad"
] | https://huggingface.co/papers/2405.19687 | 2 | 1 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=93ktalFvnJ | @inproceedings{
ko2024boosting,
title={Boosting Alignment for Post-Unlearning Text-to-Image Generative Models},
author={Myeongseob Ko and Henry Li and Zhun Wang and Jonathan Patsenker and Jiachen T. Wang and Qinbin Li and Ming Jin and Dawn Song and Ruoxi Jia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=93ktalFvnJ}
} | Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns. Driven by these concerns, machine unlearning has become crucial to effectively purge undesirable knowledge from models. While existing literature has studied various unlearning techniques, these often suffer from either poor unlearning quality or degradation in text-image alignment after unlearning, due to the competitive nature of these objectives. To address these challenges, we propose a framework that seeks an optimal model update at each unlearning iteration, ensuring monotonic improvement on both objectives. We further derive the characterization of such an update.
In addition, we design procedures to strategically diversify the unlearning and remaining datasets to boost performance improvement. Our evaluation demonstrates that our method effectively removes target classes from recent diffusion-based generative models and concepts from stable diffusion models while maintaining close alignment with the models' original trained states, thus outperforming state-of-the-art baselines. | Boosting Alignment for Post-Unlearning Text-to-Image Generative Models | [
"Myeongseob Ko",
"Henry Li",
"Zhun Wang",
"Jonathan Patsenker",
"Jiachen T. Wang",
"Qinbin Li",
"Ming Jin",
"Dawn Song",
"Ruoxi Jia"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=93gz2lmFtm | @inproceedings{
lentsch2024union,
title={{UNION}: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes},
author={Ted Lentsch and Holger Caesar and Dariu Gavrila},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=93gz2lmFtm}
} | Unsupervised 3D object detection methods have emerged to leverage vast amounts of data without requiring manual labels for training. Recent approaches rely on dynamic objects for learning to detect mobile objects but penalize the detections of static instances during training. Multiple rounds of (self) training are used to add detected static instances to the set of training targets; this procedure to improve performance is computationally expensive. To address this, we propose the method UNION. We use spatial clustering and self-supervised scene flow to obtain a set of static and dynamic object proposals from LiDAR. Subsequently, object proposals' visual appearances are encoded to distinguish static objects in the foreground and background by selecting static instances that are visually similar to dynamic objects. As a result, static and dynamic mobile objects are obtained together, and existing detectors can be trained with a single training. In addition, we extend 3D object discovery to detection by using object appearance-based cluster labels as pseudo-class labels for training object classification. We conduct extensive experiments on the nuScenes dataset and increase the state-of-the-art performance for unsupervised 3D object discovery, i.e. UNION more than doubles the average precision to 38.4. The code is available at github.com/TedLentsch/UNION. | UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes | [
"Ted Lentsch",
"Holger Caesar",
"Dariu Gavrila"
] | NeurIPS.cc/2024/Conference | 2405.15688 | [
"https://github.com/tedlentsch/union"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=93HCE8vTye | @inproceedings{
barbero2024transformers,
title={Transformers need glasses! Information over-squashing in language tasks},
author={Federico Barbero and Andrea Banino and Steven Kapturowski and Dharshan Kumaran and Jo{\~a}o Guilherme Madeira Ara{\'u}jo and Alex Vitvitskyi and Razvan Pascanu and Petar Veli{\v{c}}kovi{\'c}},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=93HCE8vTye}
} | We study how information propagates in decoder-only Transformers, which are the architectural foundation of most existing frontier large language models (LLMs). We rely on a theoretical signal propagation analysis---specifically, we analyse the representations of the last token in the final layer of the Transformer, as this is the representation used for next-token prediction. Our analysis reveals a representational collapse phenomenon: we prove that certain distinct pairs of inputs to the Transformer can yield arbitrarily close representations in the final token. This effect is exacerbated by the low-precision floating-point formats frequently used in modern LLMs. As a result, the model is provably unable to respond to these sequences in different ways---leading to errors in, e.g., tasks involving counting or copying. Further, we show that decoder-only Transformer language models can lose sensitivity to specific tokens in the input, which relates to the well-known phenomenon of over-squashing in graph neural networks. We provide empirical evidence supporting our claims on contemporary LLMs. Our theory points to simple solutions towards ameliorating these issues. | Transformers need glasses! Information over-squashing in language tasks | [
"Federico Barbero",
"Andrea Banino",
"Steven Kapturowski",
"Dharshan Kumaran",
"João Guilherme Madeira Araújo",
"Alex Vitvitskyi",
"Razvan Pascanu",
"Petar Veličković"
] | NeurIPS.cc/2024/Conference | 2406.04267 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=938EYYewtq | @inproceedings{
acosta2024global,
title={Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems},
author={Francisco Acosta and Fatih Dinc and William T Redman and Manu Madhav and David Klindt and Nina Miolane},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=938EYYewtq}
} | Grid cells in the mammalian brain are fundamental to spatial navigation, and therefore crucial to how animals perceive and interact with their environment. Traditionally, grid cells are thought support path integration through highly symmetric hexagonal lattice firing patterns. However, recent findings show that their firing patterns become distorted in the presence of significant spatial landmarks such as rewarded locations. This introduces a novel perspective of dynamic, subjective, and action-relevant interactions between spatial representations and environmental cues. Here, we propose a practical and theoretical framework to quantify and explain these interactions. To this end, we train path-integrating recurrent neural networks (piRNNs) on a spatial navigation task, whose goal is to predict the agent's position with a special focus on rewarded locations. Grid-like neurons naturally emerge from the training of piRNNs, which allows us to investigate how the two aspects of the task, space and reward, are integrated in their firing patterns. We find that geometry, but not topology, of the grid cell population code becomes distorted. Surprisingly, these distortions are global in the firing patterns of the grid cells despite local changes in the reward. Our results indicate that after training with location-specific reward information, the preserved representational topology supports successful path integration, whereas the emergent heterogeneity in individual responses due to global distortions may encode dynamically changing environmental cues. By bridging the gap between computational models and the biological reality of spatial navigation under reward information, we offer new insights into how neural systems prioritize environmental landmarks in their spatial navigation code. | Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems | [
"Francisco Acosta",
"Fatih Dinc",
"William T Redman",
"Manu Madhav",
"David Klindt",
"Nina Miolane"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=92vVuJVLVW | @inproceedings{
wang2024clusterlearngene,
title={Cluster-Learngene: Inheriting Adaptive Clusters for Vision Transformers},
author={Qiufeng Wang and Xu Yang and Fu Feng and Jing wang and Xin Geng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=92vVuJVLVW}
} | In recent years, the merging of vast datasets with powerful computational resources has led to the emergence of large pre-trained models in the field of deep learning. However, the common practices often overgeneralize the applicability of these models, overlooking the task-specific resource constraints. To mitigate this issue, we propose \textbf{Cluster-Learngene}, which effectively clusters critical internal modules from a large ancestry model and then inherits them to initialize descendant models of elastic scales. Specifically, based on the density characteristics of attention heads, our method adaptively clusters attention heads of each layer and position-wise feed-forward networks (FFNs) in the ancestry model as the learngene. Moreover, we introduce priority weight-sharing and learnable parameter transformations that expand the learngene to initialize descendant models of elastic scales. Through extensive experimentation, we demonstrate that Cluster-Learngene not only is more efficient compared to other initialization methods but also customizes models of elastic scales according to downstream task resources. | Cluster-Learngene: Inheriting Adaptive Clusters for Vision Transformers | [
"Qiufeng Wang",
"Xu Yang",
"Fu Feng",
"Jing wang",
"Xin Geng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=90IpKvVdXd | @inproceedings{
filmus2024banditfeedback,
title={Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs},
author={Yuval Filmus and Steve Hanneke and Idan Mehalel and Shay Moran},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=90IpKvVdXd}
} | Consider the domain of multiclass classification within the adversarial online setting. What is the price of relying on bandit feedback as opposed to full information? To what extent can an adaptive adversary amplify the loss compared to an oblivious one? To what extent can a randomized learner reduce the loss compared to a deterministic one? We study these questions in the mistake bound model and provide nearly tight answers.
We demonstrate that the optimal mistake bound under bandit feedback is at most $O(k)$ times higher than the optimal mistake bound in the full information case, where $k$ represents the number of labels. This bound is tight and provides an answer to an open question previously posed and studied by Daniely and Helbertal ['13] and by Long ['17, '20], who focused on deterministic learners.
Moreover, we present nearly optimal bounds of $\tilde{\Theta}(k)$ on the gap between randomized and deterministic learners, as well as between adaptive and oblivious adversaries in the bandit feedback setting. This stands in contrast to the full information scenario, where adaptive and oblivious adversaries are equivalent, and the gap in mistake bounds between randomized and deterministic learners is a constant multiplicative factor of $2$.
In addition, our results imply that in some cases the optimal randomized mistake bound is approximately the square-root of its deterministic parallel. Previous results show that this is essentially the smallest it can get.
Some of our results are proved via a reduction to prediction with expert advice under bandit feedback, a problem interesting on its own right. For this problem, we provide a randomized algorithm which is nearly optimal in some scenarios. | Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs | [
"Yuval Filmus",
"Steve Hanneke",
"Idan Mehalel",
"Shay Moran"
] | NeurIPS.cc/2024/Conference | 2402.07453 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8zg9sO4ttV | @inproceedings{
jenner2024evidence,
title={Evidence of Learned Look-Ahead in a Chess-Playing Neural Network},
author={Erik Jenner and Shreyas Kapur and Vasil Georgiev and Cameron Allen and Scott Emmons and Stuart Russell},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8zg9sO4ttV}
} | Do neural networks learn to implement algorithms such as look-ahead or search "in the wild"? Or do they rely purely on collections of simple heuristics? We present evidence of *learned look-ahead* in the policy and value network of Leela Chess Zero, the currently strongest deep neural chess engine. We find that Leela internally represents future optimal moves and that these representations are crucial for its final output in certain board states. Concretely, we exploit the fact that Leela is a transformer that treats every chessboard square like a token in language models, and give three lines of evidence: (1) activations on certain squares of future moves are unusually important causally; (2) we find attention heads that move important information "forward and backward in time," e.g., from squares of future moves to squares of earlier ones; and (3) we train a simple probe that can predict the optimal move 2 turns ahead with 92% accuracy (in board states where Leela finds a single best line). These findings are clear evidence of learned look-ahead in neural networks and might be a step towards a better understanding of their capabilities. | Evidence of Learned Look-Ahead in a Chess-Playing Neural Network | [
"Erik Jenner",
"Shreyas Kapur",
"Vasil Georgiev",
"Cameron Allen",
"Scott Emmons",
"Stuart Russell"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8z4isrqbcf | @inproceedings{
zhao2024cvvae,
title={{CV}-{VAE}: A Compatible Video {VAE} for Latent Generative Video Models},
author={Sijie Zhao and Yong Zhang and Xiaodong Cun and Shaoshu Yang and Muyao Niu and Xiaoyu Li and Wenbo Hu and Ying Shan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8z4isrqbcf}
} | Spatio-temporal compression of videos, utilizing networks such as Variational Autoencoders (VAE), plays a crucial role in OpenAI's SORA and numerous other video generative models. For instance, many LLM-like video models learn the distribution of discrete tokens derived from 3D VAEs within the VQVAE framework, while most diffusion-based video models capture the distribution of continuous latent extracted by 2D VAEs without quantization. The temporal compression is simply realized by uniform frame sampling which results in unsmooth motion between consecutive frames. Currently, there lacks of a commonly used continuous video (3D) VAE for latent diffusion-based video models in the research community. Moreover, since current diffusion-based approaches are often implemented using pre-trained text-to-image (T2I) models, directly training a video VAE without considering the compatibility with existing T2I models will result in a latent space gap between them, which will take huge computational resources for training to bridge the gap even with the T2I models as initialization. To address this issue, we propose a method for training a video VAE of latent video models, namely CV-VAE, whose latent space is compatible with that of a given image VAE, e.g., image VAE of Stable Diffusion (SD). The compatibility is achieved by the proposed novel latent space regularization, which involves formulating a regularization loss using the image VAE. Benefiting from the latent space compatibility, video models can be trained seamlessly from pre-trained T2I or video models in a truly spatio-temporally compressed latent space, rather than simply sampling video frames at equal intervals. To improve the training efficiency, we also design a novel architecture for the video VAE. With our CV-VAE, existing video models can generate four times more frames with minimal finetuning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed video VAE. | CV-VAE: A Compatible Video VAE for Latent Generative Video Models | [
"Sijie Zhao",
"Yong Zhang",
"Xiaodong Cun",
"Shaoshu Yang",
"Muyao Niu",
"Xiaoyu Li",
"Wenbo Hu",
"Ying Shan"
] | NeurIPS.cc/2024/Conference | 2405.20279 | [
"https://github.com/ailab-cvc/cv-vae"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8x48XFLvyd | @inproceedings{
mcnamara2024globally,
title={Globally Convergent Variational Inference},
author={Declan McNamara and Jackson Loper and Jeffrey Regier},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8x48XFLvyd}
} | In variational inference (VI), an approximation of the posterior distribution is selected from a family of distributions through numerical optimization. With the most common variational objective function, known as the evidence lower bound (ELBO), only convergence to a *local* optimum can be guaranteed. In this work, we instead establish the *global* convergence of a particular VI method. This VI method, which may be considered an instance of neural posterior estimation (NPE), minimizes an expectation of the inclusive (forward) KL divergence to fit a variational distribution that is parameterized by a neural network. Our convergence result relies on the neural tangent kernel (NTK) to characterize the gradient dynamics that arise from considering the variational objective in function space. In the asymptotic regime of a fixed, positive-definite neural tangent kernel, we establish conditions under which the variational objective admits a unique solution in a reproducing kernel Hilbert space (RKHS). Then, we show that the gradient descent dynamics in function space converge to this unique function. In ablation studies and practical problems, we demonstrate that our results explain the behavior of NPE in non-asymptotic finite-neuron settings, and show that NPE outperforms ELBO-based optimization, which often converges to shallow local optima. | Globally Convergent Variational Inference | [
"Declan McNamara",
"Jackson Loper",
"Jeffrey Regier"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8wvH0RZPsG | @inproceedings{
wang2024conformalized,
title={Conformalized Multiple Testing after Data-dependent Selection},
author={Xiaoning Wang and Yuyang Huo and Liuhua Peng and Changliang Zou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8wvH0RZPsG}
} | The task of distinguishing individuals of interest from a vast pool of candidates using predictive models has garnered significant attention in recent years. This task can be framed as a *conformalized multiple testing* procedure, which aims at quantifying prediction uncertainty by controlling the false discovery rate (FDR) via conformal inference. In this paper, we tackle the challenge of conformalized multiple testing after data-dependent selection procedures. To guarantee the construction of valid test statistics that accurately capture the distorted distribution resulting from the selection process, we leverage a holdout labeled set to closely emulate the selective distribution. Our approach involves adaptively picking labeled data to create a calibration set based on the stability of the selection rule. This strategy ensures that the calibration data and the selected test unit are exchangeable, allowing us to develop valid conformal p-values. Implementing with the famous Benjamini-Hochberg (BH) procedure, it effectively controls the FDR over the selected subset. To handle the randomness of the selected subset and the dependence among the constructed p-values, we establish a unified theoretical framework. This framework extends the application of conformalized multiple testing to complex selective settings. Furthermore, we conduct numerical studies to showcase the effectiveness and validity of our procedures across various scenarios. | Conformalized Multiple Testing after Data-dependent Selection | [
"Xiaoning Wang",
"Yuyang Huo",
"Liuhua Peng",
"Changliang Zou"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8vCs5U9Hbt | @inproceedings{
shen2024goalign,
title={{GO}4Align: Group Optimization for Multi-Task Alignment},
author={Jiayi Shen and Cheems Wang and Zehao Xiao and Nanne Van Noord and Marcel Worring},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8vCs5U9Hbt}
} | This paper proposes **GO4Align**, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we design an adaptive group risk minimization strategy, comprising two techniques in implementation: (i) dynamical group assignment, which clusters similar tasks based on task interactions; (ii) risk-guided group indicators, which exploit consistent task correlations with risk information from previous iterations. Comprehensive experimental results on diverse benchmarks demonstrate our method's performance superiority with even lower computational costs. | GO4Align: Group Optimization for Multi-Task Alignment | [
"Jiayi Shen",
"Cheems Wang",
"Zehao Xiao",
"Nanne Van Noord",
"Marcel Worring"
] | NeurIPS.cc/2024/Conference | 2404.06486 | [
"https://github.com/autumn9999/go4align"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8ugOlbjJpp | @inproceedings{
bassily2024private,
title={Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry},
author={Raef Bassily and Crist{\'o}bal A Guzm{\'a}n and Michael Menart},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8ugOlbjJpp}
} | In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of $(\epsilon,\delta)$-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the $\ell_p/\ell_q$ setup, $p,q\in[1,2]$. That is, we consider the case where the primal problem has an $\ell_p$-setup (i.e., the primal parameter is constrained to an $\ell_p$ bounded domain and the loss is $\ell_p$-Lipschitz with respect to the primal parameter) and the dual problem has an $\ell_q$ setup. Here, we obtain a bound of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$ on the strong SP-gap, where $n$ is the number of samples and $d$ is the dimension. This rate is nearly optimal for any $p,q\in[1,2]$. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when $p=q=2$ (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of $p$ and $q$ and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever $p,q\in[1,2]$. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider $\ell_p$-setups, $p\in[1,2]$. Here, we provide the first analysis which obtains a bound on the strong VI-gap of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$. For $p-1=\Omega(1)$, this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularization framework to SVIs. | Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry | [
"Raef Bassily",
"Cristóbal A Guzmán",
"Michael Menart"
] | NeurIPS.cc/2024/Conference | 2411.05198 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8tOYl6WsGY | @inproceedings{
zhang2024boostadapter,
title={BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping},
author={Taolin Zhang and Jinpeng Wang and Hang Guo and Tao Dai and Bin Chen and Shu-Tao Xia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8tOYl6WsGY}
} | Adaptation of
pretrained vision-language models such as CLIP to various downstream tasks have raised great interest in recent researches.
Previous works have proposed a variety of test-time adaptation (TTA) methods to achieve strong generalization without any knowledge of the target domain.
However, existing training-required TTA approaches like TPT necessitate entropy minimization that involves large computational overhead, while training-free methods like TDA overlook the potential for information mining from the test samples themselves.
In this paper, we break down the design of existing popular training-required and training-free TTA methods and bridge the gap between them within our framework.
Specifically, we maintain a light-weight key-value memory for feature retrieval from instance-agnostic historical samples and instance-aware boosting samples.
The historical samples are filtered from the testing data stream and serve to extract useful information from the target distribution, while the boosting samples are drawn from regional bootstrapping and capture the knowledge of the test sample itself.
We theoretically justify the rationality behind our method and empirically verify its effectiveness on both the out-of-distribution and the cross-domain datasets, showcasing its applicability in real-world situations. | BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping | [
"Taolin Zhang",
"Jinpeng Wang",
"Hang Guo",
"Tao Dai",
"Bin Chen",
"Shu-Tao Xia"
] | NeurIPS.cc/2024/Conference | 2410.15430 | [
"https://github.com/taolinzhang/boostadapter"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8rcFOqEud5 | @inproceedings{
zhang2024restmcts,
title={Re{ST}-{MCTS}*: {LLM} Self-Training via Process Reward Guided Tree Search},
author={Dan Zhang and Sining Zhoubian and Ziniu Hu and Yisong Yue and Yuxiao Dong and Jie Tang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8rcFOqEud5}
} | Recent methodologies in LLM self-training mostly rely on LLM generating responses and filtering those with correct output answers as training data. This approach often yields a low-quality fine-tuning training set (e.g., incorrect plans or intermediate reasoning). In this paper, we develop a reinforced self-training approach, called ReST-MCTS*, based on integrating process reward guidance with tree search MCTS* for collecting higher-quality reasoning traces as well as per-step value to train policy and reward models. ReST-MCTS* circumvents the per-step manual annotation typically used to train process rewards by tree-search-based reinforcement learning: Given oracle final correct answers, ReST-MCTS* is able to infer the correct process rewards by estimating the probability this step can help lead to the correct answer. These inferred rewards serve dual purposes: they act as value targets for further refining the process reward model and also facilitate the selection of high-quality traces for policy model self-training. We first show that the tree-search policy in ReST-MCTS* achieves higher accuracy compared with prior LLM reasoning baselines such as Best-of-N and Tree-of-Thought, within the same search budget. We then show that by using traces searched by this tree-search policy as training data, we can continuously enhance the three language models for multiple iterations, and outperform other self-training algorithms such as ReST$^\text{EM}$ and Self-Rewarding LM. | ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search | [
"Dan Zhang",
"Sining Zhoubian",
"Ziniu Hu",
"Yisong Yue",
"Yuxiao Dong",
"Jie Tang"
] | NeurIPS.cc/2024/Conference | 2406.03816 | [
"https://github.com/THUDM/ReST-MCTS"
] | https://huggingface.co/papers/2406.03816 | 0 | 1 | 0 | 5 | [] | [
"rawsh/magpie-ultra-v0.1-PRM-data-base"
] | [] | [] | [
"rawsh/magpie-ultra-v0.1-PRM-data-base"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=8qu52Fl1Dt | @inproceedings{
gong2024neuroclips,
title={NeuroClips: Towards High-fidelity and Smooth f{MRI}-to-Video Reconstruction},
author={Zixuan Gong and Guangyin Bao and Qi Zhang and Zhongwei Wan and Duoqian Miao and Shoujin Wang and Lei Zhu and Changwei Wang and Rongtao Xu and Liang Hu and Ke Liu and Yu Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8qu52Fl1Dt}
} | Reconstruction of static visual stimuli from non-invasion brain activity fMRI achieves great success, owning to advanced deep learning models such as CLIP and Stable Diffusion. However, the research on fMRI-to-video reconstruction remains limited since decoding the spatiotemporal perception of continuous visual experiences is formidably challenging. We contend that the key to addressing these challenges lies in accurately decoding both high-level semantics and low-level perception flows, as perceived by the brain in response to video stimuli. To the end, we propose NeuroClips, an innovative framework to decode high-fidelity and smooth video from fMRI. NeuroClips utilizes a semantics reconstructor to reconstruct video keyframes, guiding semantic accuracy and consistency, and employs a perception reconstructor to capture low-level perceptual details, ensuring video smoothness. During inference, it adopts a pre-trained T2V diffusion model injected with both keyframes and low-level perception flows for video reconstruction. Evaluated on a publicly available fMRI-video dataset, NeuroClips achieves smooth high-fidelity video reconstruction of up to 6s at 8FPS, gaining significant improvements over state-of-the-art models in various metrics, e.g., a 128% improvement in SSIM and an 81% improvement in spatiotemporal metrics. Our project is available at https://github.com/gongzix/NeuroClips. | NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction | [
"Zixuan Gong",
"Guangyin Bao",
"Qi Zhang",
"Zhongwei Wan",
"Duoqian Miao",
"Shoujin Wang",
"Lei Zhu",
"Changwei Wang",
"Rongtao Xu",
"Liang Hu",
"Ke Liu",
"Yu Zhang"
] | NeurIPS.cc/2024/Conference | 2410.19452 | [
"https://github.com/gongzix/neuroclips"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=8qEkjSEdls | @inproceedings{
lee2024offpolicy,
title={Off-policy estimation with adaptively collected data: the power of online learning},
author={Jeonghwan Lee and Cong Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8qEkjSEdls}
} | We consider estimation of a linear functional of the treatment effect from adaptively collected data. This problem finds a variety of applications including off-policy evaluation in contextual bandits, and estimation of the average treatment effect in causal inference. While a certain class of augmented inverse propensity weighting (AIPW) estimators enjoys desirable asymptotic properties including the semi-parametric efficiency, much less is known about their non-asymptotic theory with adaptively collected data. To fill in the gap, we first present generic upper bounds on the mean-squared error of the class of AIPW estimators that crucially depends on a sequentially weighted error between the treatment effect and its estimates. Motivated by this, we propose a general reduction scheme that allows one to produce a sequence of estimates for the treatment effect via online learning to minimize the sequentially weighted estimation error. To illustrate this, we provide three concrete instantiations in (1) the tabular case; (2) the case of linear function approximation; and (3) the case of general function approximation for the outcome model. We then provide a local minimax lower bound to show the instance-dependent optimality of the AIPW estimator using no-regret online learning algorithms. | Off-policy estimation with adaptively collected data: the power of online learning | [
"Jeonghwan Lee",
"Cong Ma"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8puv3c9CPg | @inproceedings{
lepori2024beyond,
title={Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects},
author={Michael A. Lepori and Alexa R. Tartaglini and Wai Keen Vong and Thomas Serre and Brenden Lake and Ellie Pavlick},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8puv3c9CPg}
} | Though vision transformers (ViTs) have achieved state-of-the-art performance in a variety of settings, they exhibit surprising failures when performing tasks involving visual relations. This begs the question: how do ViTs attempt to perform tasks that require computing visual relations between objects? Prior efforts to interpret ViTs tend to focus on characterizing relevant low-level visual features. In contrast, we adopt methods from mechanistic interpretability to study the higher-level visual algorithms that ViTs use to perform abstract visual reasoning. We present a case study of a fundamental, yet surprisingly difficult, relational reasoning task: judging whether two visual entities are the same or different. We find that pretrained ViTs fine-tuned on this task often exhibit two qualitatively different stages of processing despite having no obvious inductive biases to do so: 1) a perceptual stage wherein local object features are extracted and stored in a disentangled representation, and 2) a relational stage wherein object representations are compared. In the second stage, we find evidence that ViTs can learn to represent somewhat abstract visual relations, a capability that has long been considered out of reach for artificial neural networks. Finally, we demonstrate that failures at either stage can prevent a model from learning a generalizable solution to our fairly simple tasks. By understanding ViTs in terms of discrete processing stages, one can more precisely diagnose and rectify shortcomings of existing and future models. | Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects | [
"Michael A. Lepori",
"Alexa R. Tartaglini",
"Wai Keen Vong",
"Thomas Serre",
"Brenden Lake",
"Ellie Pavlick"
] | NeurIPS.cc/2024/Conference | 2406.15955 | [
"https://github.com/alexatartaglini/relational-circuits"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8pRemr5kEi | @inproceedings{
lu2024visual,
title={Visual Prompt Tuning in Null Space for Continual Learning},
author={Yue Lu and Shizhou Zhang and De Cheng and Yinghui Xing and Nannan Wang and PENG WANG and Yanning Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8pRemr5kEi}
} | Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL), by selecting and updating relevant prompts in the vision-transformer models. On the contrary, this paper aims to learn each task by tuning the prompts in the direction orthogonal to the subspace spanned by previous tasks' features, so as to ensure no interference on tasks that have been learned to overcome catastrophic forgetting in CL. However, different from the orthogonal projection in the traditional CNN architecture, the prompt gradient orthogonal projection in the ViT architecture shows completely different and greater challenges, i.e., 1) the high-order and non-linear self-attention operation; 2) the drift of prompt distribution brought by the LayerNorm in the transformer block. Theoretically, we have finally deduced two consistency conditions to achieve the prompt gradient orthogonal projection, which provide a theoretical guarantee of eliminating interference on previously learned knowledge via the self-attention mechanism in visual prompt tuning. In practice, an effective null-space-based approximation solution has been proposed to implement the prompt gradient orthogonal projection. Extensive experimental results demonstrate the effectiveness of anti-forgetting on four class-incremental benchmarks with diverse pre-trained baseline models, and our approach achieves superior performances to state-of-the-art methods. Our code is available in the supplemental material. | Visual Prompt Tuning in Null Space for Continual Learning | [
"Yue Lu",
"Shizhou Zhang",
"De Cheng",
"Yinghui Xing",
"Nannan Wang",
"PENG WANG",
"Yanning Zhang"
] | NeurIPS.cc/2024/Conference | 2406.05658 | [
"https://github.com/zugexiaodui/vptinnsforcl"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8on9dIUh5v | @inproceedings{
oh2024provable,
title={Provable Benefit of Cutout and CutMix for Feature Learning},
author={Junsoo Oh and Chulhee Yun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8on9dIUh5v}
} | Patch-level data augmentation techniques such as Cutout and CutMix have demonstrated significant efficacy in enhancing the performance of vision tasks. However, a comprehensive theoretical understanding of these methods remains elusive. In this paper, we study two-layer neural networks trained using three distinct methods: vanilla training without augmentation, Cutout training, and CutMix training. Our analysis focuses on a feature-noise data model, which consists of several label-dependent features of varying rarity and label-independent noises of differing strengths. Our theorems demonstrate that Cutout training can learn low-frequency features that vanilla training cannot, while CutMix training can learn even rarer features that Cutout cannot capture. From this, we establish that CutMix yields the highest test accuracy among the three. Our novel analysis reveals that CutMix training makes the network learn all features and noise vectors "evenly" regardless of the rarity and strength, which provides an interesting insight into understanding patch-level augmentation. | Provable Benefit of Cutout and CutMix for Feature Learning | [
"Junsoo Oh",
"Chulhee Yun"
] | NeurIPS.cc/2024/Conference | 2410.23672 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=8ohsbxw7q8 | @inproceedings{
liu2024graph,
title={Graph Diffusion Policy Optimization},
author={Yijing Liu and Chao Du and Tianyu Pang and Chongxuan Li and Min Lin and Wei Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8ohsbxw7q8}
} | Recent research has made significant progress in optimizing diffusion models for downstream objectives, which is an important pursuit in fields such as graph generation for drug design. However, directly applying these models to graph presents challenges, resulting in suboptimal performance. This paper introduces graph diffusion policy optimization (GDPO), a novel approach to optimize graph diffusion models for arbitrary (e.g., non-differentiable) objectives using reinforcement learning. GDPO is based on an eager policy gradient tailored for graph diffusion models, developed through meticulous analysis and promising improved performance. Experimental results show that GDPO achieves state-of-the-art performance in various graph generation tasks with complex and diverse objectives. Code is available at https://github.com/sail-sg/GDPO. | Graph Diffusion Policy Optimization | [
"Yijing Liu",
"Chao Du",
"Tianyu Pang",
"Chongxuan Li",
"Min Lin",
"Wei Chen"
] | NeurIPS.cc/2024/Conference | 2402.16302 | [
"https://github.com/sail-sg/gdpo"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8oSY3rA9jY | @inproceedings{
bhaskar2024finding,
title={Finding Transformer Circuits With Edge Pruning},
author={Adithya Bhaskar and Alexander Wettig and Dan Friedman and Danqi Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8oSY3rA9jY}
} | The path to interpreting a language model often proceeds via analysis of circuits---sparse computational subgraphs of the model that capture specific aspects of its behavior. Recent work has automated the task of discovering circuits. Yet, these methods have practical limitations, as they either rely on inefficient search algorithms or inaccurate approximations. In this paper, we frame circuit discovery as an optimization problem and propose _Edge Pruning_ as an effective and scalable solution. Edge Pruning leverages gradient-based pruning techniques, but instead of removing neurons or components, prunes the _edges_ between components. Our method finds circuits in GPT-2 that use less than half the number of edges than circuits found by previous methods while being equally faithful to the full model predictions on standard circuit-finding tasks. Edge Pruning is efficient on tasks involving up to 100,000 examples, outperforming previous methods in speed and producing substantially better circuits. It also perfectly recovers the ground-truth circuits in two models compiled with Tracr. Thanks to its efficiency, we scale Edge Pruning to CodeLlama-13B, a model over 100x the size of GPT-2.
We use this setting for a case study, where we compare the mechanisms behind instruction prompting and in-context learning.
We find two circuits with more than 99.96% sparsity that match the performance of the full model. Further analysis reveals that the mechanisms in the two settings overlap substantially. This shows that Edge Pruning is a practical and scalable tool for interpretability,
which can shed light on behaviors that only emerge in large models. | Finding Transformer Circuits With Edge Pruning | [
"Adithya Bhaskar",
"Alexander Wettig",
"Dan Friedman",
"Danqi Chen"
] | NeurIPS.cc/2024/Conference | 2406.16778 | [
"https://github.com/princeton-nlp/edge-pruning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=8moTQjfqAV | @inproceedings{
guan2024temporaldifference,
title={Temporal-Difference Learning Using Distributed Error Signals},
author={Jonas Guan and Shon Eduard Verch and Claas A Voelcker and Ethan C Jackson and Nicolas Papernot and William A Cunningham},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8moTQjfqAV}
} | A computational problem in biological reward-based learning is how credit assignment is performed in the nucleus accumbens (NAc). Much research suggests that NAc dopamine encodes temporal-difference (TD) errors for learning value predictions. However, dopamine is synchronously distributed in regionally homogeneous concentrations, which does not support explicit credit assignment (like used by backpropagation). It is unclear whether distributed errors alone are sufficient for synapses to make coordinated updates to learn complex, nonlinear reward-based learning tasks. We design a new deep Q-learning algorithm, Artificial Dopamine, to computationally demonstrate that synchronously distributed, per-layer TD errors may be sufficient to learn surprisingly complex RL tasks. We empirically evaluate our algorithm on MinAtar, the DeepMind Control Suite, and classic control tasks, and show it often achieves comparable performance to deep RL algorithms that use backpropagation. | Temporal-Difference Learning Using Distributed Error Signals | [
"Jonas Guan",
"Shon Eduard Verch",
"Claas A Voelcker",
"Ethan C Jackson",
"Nicolas Papernot",
"William A Cunningham"
] | NeurIPS.cc/2024/Conference | 2411.03604 | [
"https://github.com/social-ai-uoft/ad-paper"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8mZc259r8X | @inproceedings{
cheng2024learning,
title={Learning Cut Generating Functions for Integer Programming},
author={Hongyu Cheng and Amitabh Basu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8mZc259r8X}
} | The branch-and-cut algorithm is the method of choice to solve large scale integer programming problems in practice. A key ingredient of branch-and-cut is the use of *cutting planes* which are derived constraints that reduce the search space for an optimal solution. Selecting effective cutting planes to produce small branch-and-cut trees is a critical challenge in the branch-and-cut algorithm. Recent advances have employed a data-driven approach to select good cutting planes from a parameterized family, aimed at reducing the branch-and-bound tree size (in expectation) for a given distribution of integer programming instances. We extend this idea to the selection of the best cut generating function (CGF), which is a tool in the integer programming literature for generating a wide variety of cutting planes that generalize the well-known Gomory Mixed-Integer (GMI) cutting planes. We provide rigorous sample complexity bounds for the selection of an effective CGF from certain parameterized families that provably performs well for any specified distribution on the problem instances. Our empirical results show that the selected CGF can outperform the GMI cuts for certain distributions. Additionally, we explore the sample complexity of using neural networks for instance-dependent CGF selection. | Learning Cut Generating Functions for Integer Programming | [
"Hongyu Cheng",
"Amitabh Basu"
] | NeurIPS.cc/2024/Conference | 2405.13992 | [
"https://github.com/hongyu-cheng/learncgf"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8lcW9ltJx9 | @inproceedings{
zhu2024anypolicy,
title={Any2Policy: Learning Visuomotor Policy with Any-Modality},
author={Yichen Zhu and Zhicai Ou and Feifei Feng and Jian Tang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8lcW9ltJx9}
} | Humans can communicate and observe media with different modalities, such as texts, sounds, and images. For robots to be more generalizable embodied agents, they should be capable of following instructions and perceiving the world with adaptation to diverse modalities. Current robotic learning methodologies often focus on single-modal task specification and observation, thereby limiting their ability to process rich multi-modal information. Addressing this limitation, we present an end-to-end general-purpose multi-modal system named Any-to-Policy Embodied Agents. This system empowers robots to handle tasks using various modalities, whether in combinations like text-image, audio-image, text-point cloud, or in isolation. Our innovative approach involves training a versatile modality network that adapts to various inputs and connects with policy networks for effective control. Because of the lack of existing multi-modal robotics datasets for evaluation, we assembled a comprehensive real-world dataset encompassing 30 robotic tasks. Each task in this dataset is richly annotated across multiple modalities, providing a robust foundation for assessment. We conducted extensive validation of our proposed unified modality embodied agent using several simulation benchmarks, including Franka Kitchen, Meta-World, and Maniskill2, as well as in our real-world settings. Our experiments showcase the promising capability of building embodied agents that can adapt to diverse multi-modal in a unified framework. | Any2Policy: Learning Visuomotor Policy with Any-Modality | [
"Yichen Zhu",
"Zhicai Ou",
"Feifei Feng",
"Jian Tang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8koaqRdRYH | @inproceedings{
harrison2024improving,
title={Improving Neural Network Surface Processing with Principal Curvatures},
author={Josquin Harrison and James Benn and Maxime Sermesant},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8koaqRdRYH}
} | The modern study and use of surfaces is a research topic grounded in centuries of mathematical and empirical inquiry. From a mathematical point of view, curvature is an invariant that characterises the intrinsic geometry and the extrinsic shape of a surface. Yet, in modern applications the focus has shifted away from finding expressive representations of surfaces, and towards the design of efficient neural network architectures to process them. The literature suggests a tendency to either overlook the representation of the processed surface, or use overcomplicated representations whose ability to capture the essential features of a surface is opaque. We propose using curvature as the input of neural network architectures for surface processing, and explore this proposition through experiments making use of the shape operator. Our results show that using curvature as input leads to significant a increase in performance on segmentation and classification tasks, while allowing far less computational overhead than current methods. | Improving Neural Network Surface Processing with Principal Curvatures | [
"Josquin Harrison",
"James Benn",
"Maxime Sermesant"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8jyCRGXOr5 | @inproceedings{
schioppa2024efficient,
title={Efficient Sketches for Training Data Attribution and Studying the Loss Landscape},
author={Andrea Schioppa},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8jyCRGXOr5}
} | The study of modern machine learning models often necessitates storing vast quantities of gradients or Hessian vector products (HVPs). Traditional sketching methods struggle to scale under these memory constraints. We present a novel framework for scalable gradient and HVP sketching, tailored for modern hardware. We provide theoretical guarantees and demonstrate the power of our methods in applications like training data attribution, Hessian spectrum analysis, and intrinsic dimension computation for pre-trained language models. Our work sheds new light on the behavior of pre-trained language models, challenging assumptions about their intrinsic dimensionality and Hessian properties. | Efficient Sketches for Training Data Attribution and Studying the Loss Landscape | [
"Andrea Schioppa"
] | NeurIPS.cc/2024/Conference | 2402.03994 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8jpSenKvoS | @inproceedings{
sriramu2024fast,
title={Fast Channel Simulation via Error-Correcting Codes},
author={Sharang M. Sriramu and Rochelle Barsz and Elizabeth Polito and Aaron B. Wagner},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8jpSenKvoS}
} | We consider the design of practically-implementable schemes for the task of channel simulation. Existing methods do not scale with the
number of simultaneous uses of the channel and are therefore unable to harness the amortization gains associated with simulating many uses of the channel at once. We show how techniques from the theory of error-correcting codes can be applied to achieve scalability and hence improved performance. As an exemplar, we focus on how polar codes can be used to efficiently simulate i.i.d. copies of a class of binary-output channels. | Fast Channel Simulation via Error-Correcting Codes | [
"Sharang M. Sriramu",
"Rochelle Barsz",
"Elizabeth Polito",
"Aaron B. Wagner"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8jB6sGqvgQ | @inproceedings{
xhonneux2024efficient,
title={Efficient Adversarial Training in {LLM}s with Continuous Attacks},
author={Sophie Xhonneux and Alessandro Sordoni and Stephan G{\"u}nnemann and Gauthier Gidel and Leo Schwinn},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8jB6sGqvgQ}
} | Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet, in the context of LLMs, current methods for adversarial training are hindered by the high computational costs required to perform discrete adversarial attacks at each training iteration. We address this problem by instead calculating adversarial attacks in the continuous embedding space of the LLM, which is orders of magnitudes more efficient. We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses: the first makes the model robust on continuous embedding attacks computed on an adversarial behaviour dataset; the second ensures the usefulness of the final model by fine-tuning on utility data. Moreover, we introduce C-AdvIPO, an adversarial variant of IPO that does not require utility data for adversarially robust alignment. Our empirical evaluation on five models from different families (Gemma, Phi3, Mistral, Zephyr, Llama2) and at different scales (2B, 3.8B, 7B) shows that both algorithms substantially enhance LLM robustness against discrete attacks (GCG, AutoDAN, PAIR), while maintaining utility. Our results demonstrate that robustness to continuous perturbations can extrapolate to discrete threat models. Thereby, we present a path toward scalable adversarial training algorithms for robustly aligning LLMs. | Efficient Adversarial Training in LLMs with Continuous Attacks | [
"Sophie Xhonneux",
"Alessandro Sordoni",
"Stephan Günnemann",
"Gauthier Gidel",
"Leo Schwinn"
] | NeurIPS.cc/2024/Conference | 2405.15589 | [
"https://github.com/sophie-xhonneux/continuous-advtrain"
] | https://huggingface.co/papers/2405.15589 | 2 | 0 | 0 | 5 | [
"ContinuousAT/Zephyr-CAT",
"ContinuousAT/Phi-CAT",
"ContinuousAT/Phi-CAPO"
] | [] | [] | [
"ContinuousAT/Zephyr-CAT",
"ContinuousAT/Phi-CAT",
"ContinuousAT/Phi-CAPO"
] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=8iytZCnXIu | @inproceedings{
dittert2024bricksrl,
title={Bricks{RL}: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with {LEGO}},
author={Sebastian Dittert and Vincent Moens and Gianni De Fabritiis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8iytZCnXIu}
} | We present BricksRL, a platform designed to democratize access to robotics for reinforcement learning research and education. BricksRL facilitates the creation, design, and training of custom LEGO robots in the real world by interfacing them with the TorchRL library for reinforcement learning agents. The integration of TorchRL with the LEGO hubs, via Bluetooth bidirectional communication, enables state-of-the-art reinforcement learning training on GPUs for a wide variety of LEGO builds. This offers a flexible and cost-efficient approach for scaling and also provides a robust infrastructure for robot-environment-algorithm communication. We present various experiments across tasks and robot configurations, providing built plans and training results. Furthermore, we demonstrate that inexpensive LEGO robots can be trained end-to-end in the real world to achieve simple tasks, with training times typically under 120 minutes on a normal laptop. Moreover, we show how users can extend the capabilities, exemplified by the successful integration of non-LEGO sensors. By enhancing accessibility to both robotics and reinforcement learning, BricksRL establishes a strong foundation for democratized robotic learning in research and educational settings. | BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO | [
"Sebastian Dittert",
"Vincent Moens",
"Gianni De Fabritiis"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=8ihVBYpMV4 | @inproceedings{
li2024autoformalize,
title={Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency},
author={Zenan Li and Yifan Wu and Zhaoyu Li and Xinming Wei and Xian Zhang and Fan Yang and Xiaoxing Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8ihVBYpMV4}
} | Autoformalization, the task of automatically translating natural language descriptions into a formal language, poses a significant challenge across various domains, especially in mathematics. Recent advancements in large language models (LLMs) have unveiled their promising capabilities to formalize even competition-level math problems. However, we observe a considerable discrepancy between pass@1 and pass@k accuracies in LLM-generated formalizations. To address this gap, we introduce a novel framework that scores and selects the best result from k autoformalization candidates based on two complementary self-consistency methods: symbolic equivalence and semantic consistency. Elaborately, symbolic equivalence identifies the logical homogeneity among autoformalization candidates using automated theorem provers, and semantic consistency evaluates the preservation of the original meaning by informalizing the candidates and computing the similarity between the embeddings of the original and informalized texts.
Our extensive experiments on the MATH and miniF2F datasets demonstrate that our approach significantly enhances autoformalization accuracy, achieving up to 0.22-1.35x relative improvements across various LLMs and baseline methods. | Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency | [
"Zenan Li",
"Yifan Wu",
"Zhaoyu Li",
"Xinming Wei",
"Xian Zhang",
"Fan Yang",
"Xiaoxing Ma"
] | NeurIPS.cc/2024/Conference | 2410.20936 | [
"https://github.com/miracle-messi/isa-autoformal"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8iPobEKUUA | @inproceedings{
trabelsi2024efficient,
title={Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms},
author={Firas Trabelsi and David Vilar and Mara Finkelstein and Markus Freitag},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8iPobEKUUA}
} | Minimum Bayes Risk (MBR) decoding is a powerful decoding strategy widely used for text generation tasks but its quadratic computational complexity limits its practical application. This paper presents a novel approach for approximating MBR decoding using matrix completion techniques, focusing on a machine translation task. We formulate MBR decoding as a matrix completion problem, where the utility metric scores between candidate hypotheses and reference translations form a low-rank matrix. First we empirically show that the scores matrices indeed have a low-rank structure. Then we exploit this by only computing a random subset of the scores and efficiently recover the missing entries in the matrix by applying the Alternating Least Squares (ALS) algorithm, thereby enabling fast approximation of the MBR decoding process. Our experimental results on machine translation tasks demonstrate that the proposed method requires 1/16 utility metric computations compared to the vanilla MBR decoding while achieving equal translation quality measured by COMET on the WMT22 dataset (en<>de, en<>ru). We also benchmark our method against other approximation methods and we show significant gains in quality. | Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms | [
"Firas Trabelsi",
"David Vilar",
"Mara Finkelstein",
"Markus Freitag"
] | NeurIPS.cc/2024/Conference | 2406.02832 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8i6px5W1Rf | @inproceedings{
demircan2024evaluating,
title={Evaluating alignment between humans and neural network representations in image-based learning tasks},
author={Can Demircan and Tankred Saanum and Leonardo Pettini and Marcel Binz and Blazej M Baczkowski and Christian F. Doeller and Mona M. Garvert and Eric Schulz},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8i6px5W1Rf}
} | Humans represent scenes and objects in rich feature spaces, carrying information that allows us to generalise about category memberships and abstract functions with few examples. What determines whether a neural network model generalises like a human? We tested how well the representations of $86$ pretrained neural network models mapped to human learning trajectories across two tasks where humans had to learn continuous relationships and categories of natural images. In these tasks, both human participants and neural networks successfully identified the relevant stimulus features within a few trials, demonstrating effective generalisation. We found that while training dataset size was a core determinant of alignment with human choices, contrastive training with multi-modal data (text and imagery) was a common feature of currently publicly available models that predicted human generalisation. Intrinsic dimensionality of representations had different effects on alignment for different model types. Lastly, we tested three sets of human-aligned representations and found no consistent improvements in predictive accuracy compared to the baselines. In conclusion, pretrained neural networks can serve to extract representations for cognitive models, as they appear to capture some fundamental aspects of cognition that are transferable across tasks. Both our paradigms and modelling approach offer a novel way to quantify alignment between neural networks and humans and extend cognitive science into more naturalistic domains. | Evaluating alignment between humans and neural network representations in image-based learning tasks | [
"Can Demircan",
"Tankred Saanum",
"Leonardo Pettini",
"Marcel Binz",
"Blazej M Baczkowski",
"Christian F. Doeller",
"Mona M. Garvert",
"Eric Schulz"
] | NeurIPS.cc/2024/Conference | 2306.09377 | [
"https://github.com/candemircan/naturalcogsci"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8hBc843g1p | @inproceedings{
li2024improved,
title={Improved Generation of Adversarial Examples Against Safety-aligned {LLM}s},
author={Qizhang Li and Yiwen Guo and Wangmeng Zuo and Hao Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8hBc843g1p}
} | Adversarial prompts (or say, adversarial examples) generated using gradient-based methods exhibit outstanding performance in performing automatic jailbreak attacks against safety-aligned LLMs. Nevertheless, due to the discrete nature of texts, the input gradient of LLMs struggles to precisely reflect the magnitude of loss change that results from token replacements in the prompt, leading to limited attack success rates against safety-aligned LLMs, even in the *white-box* setting. In this paper, we explore a new perspective on this problem, suggesting that it can be alleviated by leveraging innovations inspired in transfer-based attacks that were originally proposed for attacking *black-box* image classification models. For the first time, we appropriate the ideologies of effective methods among these transfer-based attacks, *i.e.*, Skip Gradient Method and Intermediate Level Attack, into gradient-based adversarial prompt generation and achieve significant performance gains without introducing obvious computational cost. Meanwhile, by discussing mechanisms behind the gains, new insights are drawn, and proper combinations of these methods are also developed. Our empirical results show that 87% of the query-specific adversarial suffixes generated by the developed combination can induce Llama-2-7B-Chat to produce the output that exactly matches the target string on AdvBench. This match rate is 33% higher than that of a very strong baseline known as GCG, demonstrating advanced discrete optimization for adversarial prompt generation against LLMs. In addition, without introducing obvious cost, the combination achieves >30% absolute increase in attack success rates compared with GCG when generating both query-specific (38% ->68%) and universal adversarial prompts (26.68% -> 60.32%) for attacking the Llama-2-7B-Chat model on AdvBench.
Code at: https://github.com/qizhangli/Gradient-based-Jailbreak-Attacks. | Improved Generation of Adversarial Examples Against Safety-aligned LLMs | [
"Qizhang Li",
"Yiwen Guo",
"Wangmeng Zuo",
"Hao Chen"
] | NeurIPS.cc/2024/Conference | 2405.20778 | [
"https://github.com/qizhangli/Gradient-based-Jailbreak-Attacks"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8abNCVJs2j | @inproceedings{
hu2024sste,
title={S-{STE}: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training},
author={Yuezhou Hu and Jun Zhu and Jianfei Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8abNCVJs2j}
} | Training deep neural networks (DNNs) is costly. Fortunately, Nvidia Ampere and Hopper GPUs can accelerate matrix multiplications twice as fast as a dense equivalent by implementing 2:4 sparsity. However, previous STE-based 2:4 pre-training methods (\eg~STE with hard-thresholding, SR-STE) suffer from optimization difficulties because of discontinuous pruning function.
In this study, we comprehensively analyse the bottleneck of traditional N:M sparse training and recognize three drawbacks with discontinuity: incorrect descending direction, inability to predict the amount of descent and sparse mask oscillation. In the light of this statement, we propose S-STE, a simple yet powerful 2:4 training method that contains two parts: to continuously project weights to be 2:4 sparse, and to rescale sparse weights with a per-tensor fixed scaling factor. Besides, we adopt minimum-variance unbiased estimation for activation gradient and FP8 quantization for whole process. Results show that our method surpass previous 2:4 pre-training recipes and is comparable even with full parameter models. | S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training | [
"Yuezhou Hu",
"Jun Zhu",
"Jianfei Chen"
] | NeurIPS.cc/2024/Conference | 2409.09099 | [
"https://github.com/huyz2023/2by4-pretrain"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8aAaYEwNR4 | @inproceedings{
mozikov2024eai,
title={{EAI}: Emotional Decision-Making of {LLM}s in Strategic Games and Ethical Dilemmas},
author={Mikhail Mozikov and Nikita Severin and Valeria Bodishtianu and Maria Glushanina and Ivan Nasonov and Daniil Orekhov and Vladislav Pekhotin and Ivan Makovetskiy and Mikhail Baklashkin and Vasily Lavrentyev and Akim Tsvigun and Denis Turdakov and Tatiana Shavrina and Andrey Savchenko and Ilya Makarov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8aAaYEwNR4}
} | One of the urgent tasks of artificial intelligence is to assess the safety and alignment of large language models (LLMs) with human behavior. Conventional verification only in pure natural language processing benchmarks can be insufficient. Since emotions often influence human decisions, this paper examines LLM alignment in complex strategic and ethical environments, providing an in-depth analysis of the drawbacks of our psychology and the emotional impact on decision-making in humans and LLMs. We introduce the novel EAI framework for integrating emotion modeling into LLMs to examine the emotional impact on ethics and LLM-based decision-making in various strategic games, including bargaining and repeated games. Our experimental study with various LLMs demonstrated that emotions can significantly alter the ethical decision-making landscape of LLMs, highlighting the need for robust mechanisms to ensure consistent ethical standards. Our game-theoretic analysis revealed that LLMs are susceptible to emotional biases influenced by model size, alignment strategies, and primary pretraining language. Notably, these biases often diverge from typical human emotional responses, occasionally leading to unexpected drops in cooperation rates, even under positive emotional influence. Such behavior complicates the alignment of multiagent systems, emphasizing the need for benchmarks that can rigorously evaluate the degree of emotional alignment. Our framework provides a foundational basis for developing such benchmarks. | EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas | [
"Mikhail Mozikov",
"Nikita Severin",
"Valeria Bodishtianu",
"Maria Glushanina",
"Ivan Nasonov",
"Daniil Orekhov",
"Vladislav Pekhotin",
"Ivan Makovetskiy",
"Mikhail Baklashkin",
"Vasily Lavrentyev",
"Akim Tsvigun",
"Denis Turdakov",
"Tatiana Shavrina",
"Andrey Savchenko",
"Ilya Makarov"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8aA3DHLK5h | @inproceedings{
chakrabarti2024extensiveform,
title={Extensive-Form Game Solving via Blackwell Approachability on Treeplexes},
author={Darshan Chakrabarti and Julien Grand-Cl{\'e}ment and Christian Kroer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8aA3DHLK5h}
} | We introduce the first algorithmic framework for Blackwell approachability on the sequence-form polytope, the class of convex polytopes capturing the strategies of players in extensive-form games (EFGs).
This leads to a new class of regret-minimization algorithms that are stepsize-invariant, in the same sense as the Regret Matching and Regret Matching$^+$ algorithms for the simplex.
Our modular framework can be combined with any existing regret minimizer over cones to compute a Nash equilibrium in two-player zero-sum EFGs with perfect recall, through the self-play framework. Leveraging predictive online mirror descent, we introduce *Predictive Treeplex Blackwell$^+$* (PTB$^+$), and show a $O(1/\sqrt{T})$ convergence rate to Nash equilibrium in self-play. We then show how to stabilize PTB$^+$ with a stepsize, resulting in an algorithm with a state-of-the-art $O(1/T)$ convergence rate.
We provide an extensive set of experiments to compare our framework with several algorithmic benchmarks, including CFR$^+$ and its predictive variant, and we highlight interesting connections between practical performance and the stepsize-dependence or stepsize-invariance properties of classical algorithms. | Extensive-Form Game Solving via Blackwell Approachability on Treeplexes | [
"Darshan Chakrabarti",
"Julien Grand-Clément",
"Christian Kroer"
] | NeurIPS.cc/2024/Conference | 2403.04680 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=8ZLL6mu2qC | @inproceedings{
ben-basat2024optimal,
title={Optimal and Approximate Adaptive Stochastic Quantization},
author={Ran Ben-Basat and Yaniv Ben-Itzhak and Michael Mitzenmacher and shay vargaftik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8ZLL6mu2qC}
} | Quantization is a fundamental optimization for many machine learning (ML) use cases, including compressing gradients, model weights and activations, and datasets. The most accurate form of quantization is adaptive, where the error is minimized with respect to a given input rather than optimizing for the worst case. However, optimal adaptive quantization methods are considered infeasible in terms of both their runtime and memory requirements.
We revisit the Adaptive Stochastic Quantization (ASQ) problem and present algorithms that find optimal solutions with asymptotically improved time and space complexities. Our experiments indicate that our algorithms may open the door to using ASQ more extensively in a variety of ML applications. We also present an even faster approximation algorithm for quantizing large inputs on the fly. | Optimal and Approximate Adaptive Stochastic Quantization | [
"Ran Ben-Basat",
"Yaniv Ben-Itzhak",
"Michael Mitzenmacher",
"shay vargaftik"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8XoWofmZkI | @inproceedings{
yarotsky2024learnability,
title={Learnability of high-dimensional targets by two-parameter models and gradient flow},
author={Dmitry Yarotsky},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8XoWofmZkI}
} | We explore the theoretical possibility of learning $d$-dimensional targets with $W$-parameter models by gradient flow (GF) when $W<d$. Our main result shows that if the targets are described by a particular $d$-dimensional probability distribution, then there exist models with as few as two parameters that can learn the targets with arbitrarily high success probability. On the other hand, we show that for $W<d$ there is necessarily a large subset of GF-non-learnable targets. In particular, the set of learnable targets is not dense in $\mathbb R^d$, and any subset of $\mathbb R^d$ homeomorphic to the $W$-dimensional sphere contains non-learnable targets. Finally, we observe that the model in our main theorem on almost guaranteed two-parameter learning is constructed using a hierarchical procedure and as a result is not expressible by a single elementary function. We show that this limitation is essential in the sense that most models written in terms of elementary functions cannot achieve the learnability demonstrated in this theorem. | Learnability of high-dimensional targets by two-parameter models and gradient flow | [
"Dmitry Yarotsky"
] | NeurIPS.cc/2024/Conference | 2402.17089 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8W5ADJOKcv | @inproceedings{
deng2024neucmds,
title={Neuc-{MDS}: Non-Euclidean Multidimensional Scaling Through Bilinear Forms},
author={Chengyuan Deng and Jie Gao and Kevin Lu and Feng Luo and Hongbin Sun and Cheng Xin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8W5ADJOKcv}
} | We introduce \textbf{N}on-\textbf{Euc}lidean-\textbf{MDS} (Neuc-MDS), which extends Multidimensional Scaling (MDS) to generate outputs that can be non-Euclidean and non-metric. The main idea is to generalize the inner product to other symmetric bilinear forms to utilize the negative eigenvalues of dissimiliarity Gram matrices. Neuc-MDS efficiently optimizes the choice of (both positive and negative) eigenvalues of the dissimilarity Gram matrix to reduce STRESS, the sum of squared pairwise error. We provide an in-depth error analysis and proofs of the optimality in minimizing lower bounds of STRESS. We demonstrate Neuc-MDS's ability to address limitations of classical MDS raised by prior research, and test it on various synthetic and real-world datasets in comparison with both linear and non-linear dimension reduction methods. | Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms | [
"Chengyuan Deng",
"Jie Gao",
"Kevin Lu",
"Feng Luo",
"Hongbin Sun",
"Cheng Xin"
] | NeurIPS.cc/2024/Conference | 2411.10889 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8VKxTlnejE | @inproceedings{
he2024mambaad,
title={Mamba{AD}: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection},
author={Haoyang He and Yuhu Bai and Jiangning Zhang and Qingdong He and Hongxu Chen and Zhenye Gan and Chengjie Wang and Xiangtai Li and Guanzhong Tian and Lei Xie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8VKxTlnejE}
} | Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with their superior long-range modeling and linear efficiency, have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring (Locality-Enhanced State Space) LSS modules at multi-scales. The proposed LSS module, integrating parallel cascaded (Hybrid State Space) HSS blocks and multi-kernel convolutions operations, effectively captures both long-range and local information. The HSS block, utilizing (Hybrid Scanning) HS encoders, encodes feature maps into five scanning methods and eight directions, thereby strengthening global connections through the (State Space Model) SSM. The use of Hilbert scanning and eight directions significantly improves feature sequence modeling. Comprehensive experiments on six diverse anomaly detection datasets and seven metrics demonstrate state-of-the-art performance, substantiating the method's effectiveness. The code and models are available at https://lewandofskee.github.io/projects/MambaAD. | MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection | [
"Haoyang He",
"Yuhu Bai",
"Jiangning Zhang",
"Qingdong He",
"Hongxu Chen",
"Zhenye Gan",
"Chengjie Wang",
"Xiangtai Li",
"Guanzhong Tian",
"Lei Xie"
] | NeurIPS.cc/2024/Conference | 2404.06564 | [
"https://github.com/lewandofskee/mambaad"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8Uyfr5TcNR | @inproceedings{
chen2024robust,
title={Robust Reinforcement Learning with General Utility},
author={Ziyi Chen and Yan Wen and Zhengmian Hu and Heng Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8Uyfr5TcNR}
} | Reinforcement Learning (RL) problem with general utility is a powerful decision making framework that covers standard RL with cumulative cost, exploration problems, and demonstration learning. Existing works on RL with general utility do not consider the robustness under environmental perturbation, which is important to adapt RL system in the real-world environment that differs from the training environment. To train a robust policy, we propose a robust RL framework with general utility, which subsumes many existing RL frameworks including RL, robust RL, RL with general utility, constrained RL, robust constrained RL, pure exploration, robust entropy regularized RL, etc. Then we focus on popular convex utility functions, with which our proposed learning framework is a challenging nonconvex-nonconcave minimax optimization problem, and design a two-phase stochastic policy gradient type algorithm and obtain its sample complexity result for gradient convergence. Furthermore, for convex utility on a widely used polyhedral ambiguity set, we design an algorithm and obtain its convergence rate to a global optimal solution. | Robust Reinforcement Learning with General Utility | [
"Ziyi Chen",
"Yan Wen",
"Zhengmian Hu",
"Heng Huang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8UqyWNsnyA | @inproceedings{
yu2024an,
title={An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling},
author={Shenbao Yu and Yinghui Pan and Yifeng Zeng and Prashant Doshi and Guoquan Liu and Kim-Leng Poh and Mingwei Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8UqyWNsnyA}
} | Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. | An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling | [
"Shenbao Yu",
"Yinghui Pan",
"Yifeng Zeng",
"Prashant Doshi",
"Guoquan Liu",
"Kim-Leng Poh",
"Mingwei Lin"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8PWvdaRQAu | @inproceedings{
saporta2024contrasting,
title={Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities},
author={Adriel Saporta and Aahlad Manas Puli and Mark Goldstein and Rajesh Ranganath},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8PWvdaRQAu}
} | Contrastive learning methods, such as CLIP, leverage naturally paired data—for example, images and their corresponding text captions—to learn general representations that transfer efficiently to downstream tasks. While such approaches are generally applied to two modalities, domains such as robotics, healthcare, and video need to support many types of data at once. We show that the pairwise application of CLIP fails to capture joint information between modalities, thereby limiting the quality of the learned representations. To address this issue, we present Symile, a simple contrastive learning approach that captures higher-order information between any number of modalities. Symile provides a flexible, architecture-agnostic objective for learning modality-specific representations. To develop Symile's objective, we derive a lower bound on total correlation, and show that Symile representations for any set of modalities form a sufficient statistic for predicting the remaining modalities. Symile outperforms pairwise CLIP, even with modalities missing in the data, on cross-modal classification and retrieval across several experiments including on an original multilingual dataset of 33M image, text and audio samples and a clinical dataset of chest X-rays, electrocardiograms, and laboratory measurements. All datasets and code used in this work are publicly available at https://github.com/rajesh-lab/symile. | Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities | [
"Adriel Saporta",
"Aahlad Manas Puli",
"Mark Goldstein",
"Rajesh Ranganath"
] | NeurIPS.cc/2024/Conference | 2411.01053 | [
"https://github.com/rajesh-lab/symile"
] | https://huggingface.co/papers/2411.01053 | 1 | 1 | 0 | 4 | [] | [
"arsaporta/symile-m3"
] | [] | [] | [
"arsaporta/symile-m3"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=8Ofbg2KYMu | @inproceedings{
hassani2024faster,
title={Faster Neighborhood Attention: Reducing the O(n{\textasciicircum}2) Cost of Self Attention at the Threadblock Level},
author={Ali Hassani and Wen-mei Hwu and Humphrey Shi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8Ofbg2KYMu}
} | Neighborhood attention reduces the cost of self attention by restricting each token’s attention span to its nearest neighbors. This restriction, parameterized by a window size and dilation factor, draws a spectrum of possible attention patterns between linear projection and self attention. Neighborhood attention, and more generally sliding window attention patterns, have long been bounded by infrastructure, particularly in higher-rank spaces (2-D and 3-D), calling for the development of custom kernels, which have been limited in either functionality, or performance, if not both. In this work, we aim to massively improve upon existing infrastructure by providing two new methods for implementing neighborhood attention. We first show that neighborhood attention can be represented as a batched GEMM problem, similar to standard attention, and implement it for 1-D and 2-D neighborhood attention. These kernels on average provide 895% and 272% improvement in full precision runtime compared to existing naive CUDA kernels for 1-D and 2-D neighborhood attention respectively. We find that aside from being heavily bound by memory bandwidth, certain inherent inefficiencies exist in all unfused implementations of neighborhood attention, which in most cases undo their theoretical efficiency gain. Motivated by the progress made into fused dot-product attention kernels, we developed fused neighborhood attention; an adaptation of fused dot-product attention kernels that allow fine-grained control over attention across different spatial axes. Known for reducing the quadratic time complexity of self attention to a linear complexity, neighborhood attention can now enjoy a reduced and constant memory footprint, and record-breaking half precision runtime. We observe that our fused implementation successfully circumvents some of the unavoidable inefficiencies in unfused implementations. While our unfused GEMM-based kernels only improve half precision performance compared to naive kernels by an average of 548% and 193% in 1-D and 2-D problems respectively, our fused kernels improve naive kernels by an average of 1759% and 958% in 1-D and 2-D problems respectively. These improvements translate into up to 104% improvement in inference and 39% improvement in training existing models based on neighborhood attention, and additionally extend its applicability to image and video perception, as well as other modalities. Our work is open-sourced at https://github.com/SHI-Labs/NATTEN/. | Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level | [
"Ali Hassani",
"Wen-mei Hwu",
"Humphrey Shi"
] | NeurIPS.cc/2024/Conference | 2403.04690 | [
"https://github.com/shi-labs/natten"
] | https://huggingface.co/papers/2403.04690 | 0 | 1 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=8LbJfEjIrT | @inproceedings{
mahmood2024pricing,
title={Pricing and Competition for Generative {AI}},
author={Rafid Mahmood},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8LbJfEjIrT}
} | Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii) users interact with this model over a series of natural language prompts; and (iii) the model is ideally evaluated on binary user satisfaction with respect to model outputs. Given these characteristics, we explore the problem of how developers of new generative AI software can release and price their technology. We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness. We then model the pricing problem of generative AI software as a game between two different companies who sequentially release their models before users choose their preferred model for each task. Here, the price optimization problem becomes piecewise continuous where the companies must choose a subset of the tasks on which to be cost-effective and forgo revenue for the remaining tasks. In particular, we reveal the value of market information by showing that a company who deploys later after knowing their competitor’s price can always secure cost-effectiveness on at least one task, whereas the company who is the first-to-market must price their model in a way that incentivizes higher prices from the latecomer in order to gain revenue. Most importantly, we find that if the different tasks are sufficiently similar, the first-to-market model may become cost-ineffective on all tasks regardless of how this technology is priced. | Pricing and Competition for Generative AI | [
"Rafid Mahmood"
] | NeurIPS.cc/2024/Conference | 2411.02661 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8KkBxzn0km | @inproceedings{
bellitto2024saliencydriven,
title={Saliency-driven Experience Replay for Continual Learning},
author={Giovanni Bellitto and Federica Proietto Salanitri and Matteo Pennisi and Matteo Boschini and Lorenzo Bonicelli and Angelo Porrello and Simone Calderara and Simone Palazzo and Concetto Spampinato},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8KkBxzn0km}
} | We present Saliency-driven Experience Replay - SER - a biologically-plausible approach based on replicating human visual saliency to enhance classification models in continual learning settings. Inspired by neurophysiological evidence that the primary visual cortex does not contribute to object manifold untangling for categorization and that primordial saliency biases are still embedded in the modern brain, we propose to employ auxiliary saliency prediction features as a modulation signal to drive and stabilize the learning of a sequence of non-i.i.d. classification tasks. Experimental results confirm that SER effectively enhances the performance (in some cases up to about twenty percent points) of state-of-the-art continual learning methods, both in class-incremental and task-incremental settings. Moreover, we show that saliency-based modulation successfully encourages the learning of features that are more robust to the presence of spurious features and to adversarial attacks than baseline methods. Code is available at: https://github.com/perceivelab/SER | Saliency-driven Experience Replay for Continual Learning | [
"Giovanni Bellitto",
"Federica Proietto Salanitri",
"Matteo Pennisi",
"Matteo Boschini",
"Lorenzo Bonicelli",
"Angelo Porrello",
"Simone Calderara",
"Simone Palazzo",
"Concetto Spampinato"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=8KPyJm4gt5 | @inproceedings{
foster2024is,
title={Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning},
author={Dylan J Foster and Adam Block and Dipendra Misra},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8KPyJm4gt5}
} | Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied to robotics, autonomous driving, and autoregressive text generation. The simplest approach to IL, behavior cloning (BC) is thought to incur sample complexity with unfavorable quadratic dependence on the problem horizon, motivating a variety of different online algorithms that attain improved linear horizon dependence under stronger assumptions on the data and the learner’s access to the expert.
We revisit the apparent gap between offline and online IL from a learning-theoretic perspective, with a focus on general policy classes up to and including deep neural networks. Through a new analysis of BC with the logarithmic loss, we show that it is possible to achieve horizon-independent sample complexity in offline IL whenever (i) the range of the cumulative payoffs is controlled, and (ii) an appropriate notion of supervised learning complexity for the policy class is controlled. Specializing our results to deterministic, stationary policies, we show that the gap between offline and online IL is not fundamental: (i) it is possible to achieve linear dependence on horizon in offline IL under dense rewards (matching what was previously only known to be achievable in online IL); and (ii) without further assumptions on the policy class, online IL cannot improve over offline IL with the logarithmic loss, even in benign MDPs. We complement our theoretical results with experiments on standard RL tasks and autoregressive language generation to validate the practical relevance of our findings. | Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning | [
"Dylan J Foster",
"Adam Block",
"Dipendra Misra"
] | NeurIPS.cc/2024/Conference | 2407.15007 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=8K6ul0hgtC | @inproceedings{
song2024how,
title={How does {PDE} order affect the convergence of {PINN}s?},
author={Chang hoon Song and Yesom Park and Myungjoo Kang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8K6ul0hgtC}
} | This paper analyzes the inverse relationship between the order of partial differential equations (PDEs) and the convergence of gradient descent in physics-informed neural networks (PINNs) with the power of ReLU activation. The integration of the PDE into a loss function endows PINNs with a distinctive feature to require computing derivatives of model up to the PDE order. Although it has been empirically observed that PINNs encounter difficulties in convergence when dealing with high-order or high-dimensional PDEs, a comprehensive theoretical understanding of this issue remains elusive. This paper offers theoretical support for this pathological behavior by demonstrating that the gradient flow converges in a lower probability when the PDE order is higher. In addition, we show that PINNs struggle to address high-dimensional problems because the influence of dimensionality on convergence is exacerbated with increasing PDE order. To address the pathology, we use the insights garnered to consider variable splitting that decomposes the high-order PDE into a system of lower-order PDEs. We prove that by reducing the differential order, the gradient flow of variable splitting is more likely to converge to the global optimum. Furthermore, we present numerical experiments in support of our theoretical claims. | How does PDE order affect the convergence of PINNs? | [
"Chang hoon Song",
"Yesom Park",
"Myungjoo Kang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8JauriwDeH | @inproceedings{
das2024nearoptimal,
title={Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped {SGD}},
author={Aniket Das and Dheeraj Mysore Nagaraj and Soumyabrata Pal and Arun Suggala and Prateek Varshney},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8JauriwDeH}
} | $\newcommand{\Tr}{\mathsf{Tr}}$
We consider the problem of high-dimensional heavy-tailed statistical estimation in the streaming setting, which is much harder than the traditional batch setting due to memory constraints. We cast this problem as stochastic convex optimization with heavy tailed stochastic gradients, and prove that the widely used Clipped-SGD algorithm attains near-optimal sub-Gaussian statistical rates whenever the second moment of the stochastic gradient noise is finite. More precisely, with $T$ samples, we show that Clipped-SGD, for smooth and strongly convex objectives, achieves an error of $\sqrt{\frac{\Tr(\Sigma)+\sqrt{\Tr(\Sigma)\\|\Sigma\\|_2}\ln(\tfrac{\ln(T)}{\delta})}{T}}$ with probability $1-\delta$, where $\Sigma$ is the covariance of the clipped gradient. Note that the fluctuations (depending on $\tfrac{1}{\delta}$) are of lower order than the term $\Tr(\Sigma)$.
This improves upon the current best rate of
$\sqrt{\frac{\Tr(\Sigma)\ln(\tfrac{1}{\delta})}{T}}$ for Clipped-SGD, known \emph{only} for smooth and strongly convex objectives. Our results also extend to smooth convex and lipschitz convex objectives. Key to our result is a novel iterative refinement strategy for martingale concentration, improving upon the PAC-Bayes approach of \citet{catoni2018dimension}. | Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD | [
"Aniket Das",
"Dheeraj Mysore Nagaraj",
"Soumyabrata Pal",
"Arun Suggala",
"Prateek Varshney"
] | NeurIPS.cc/2024/Conference | 2410.20135 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8IysmgZte4 | @inproceedings{
zhu2024distributional,
title={Distributional Successor Features Enable Zero-Shot Policy Optimization},
author={Chuning Zhu and Xinqi Wang and Tyler Han and Simon Shaolei Du and Abhishek Gupta},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8IysmgZte4}
} | Intelligent agents must be generalists, capable of quickly adapting to various tasks. In reinforcement learning (RL), model-based RL learns a dynamics model of the world, in principle enabling transfer to arbitrary reward functions through planning. However, autoregressive model rollouts suffer from compounding error, making model-based RL ineffective for long-horizon problems. Successor features offer an alternative by modeling a policy's long-term state occupancy, reducing policy evaluation under new rewards to linear regression. Yet, policy optimization with successor features can be challenging. This work proposes a novel class of models, i.e., Distributional Successor Features for Zero-Shot Policy Optimization (DiSPOs), that learn a distribution of successor features of a stationary dataset's behavior policy, along with a policy that acts to realize different successor features within the dataset. By directly modeling long-term outcomes in the dataset, DiSPOs avoid compounding error while enabling a simple scheme for zero-shot policy optimization across reward functions. We present a practical instantiation of DiSPOs using diffusion models and show their efficacy as a new class of transferable models, both theoretically and empirically across various simulated robotics problems. Videos and code are available at https://weirdlabuw.github.io/dispo/. | Distributional Successor Features Enable Zero-Shot Policy Optimization | [
"Chuning Zhu",
"Xinqi Wang",
"Tyler Han",
"Simon Shaolei Du",
"Abhishek Gupta"
] | NeurIPS.cc/2024/Conference | 2403.06328 | [
""
] | https://huggingface.co/papers/2403.06328 | 0 | 0 | 0 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=8HwI6UavYc | @inproceedings{
bartrum2024replaceanythingd,
title={ReplaceAnything3D: Text-Guided Object Replacement in 3D Scenes with Compositional Scene Representations},
author={Edward Bartrum and Thu Nguyen-Phuoc and Chris Xie and Zhengqin Li and Numair Khan and Armen Avetisyan and Douglas Lanman and Lei Xiao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8HwI6UavYc}
} | We introduce ReplaceAnything3D model RAM3D, a novel method for 3D object replacement in 3D scenes based on users' text description. Given multi-view images of a scene, a text prompt describing the object to replace, and another describing the new object, our Erase-and-Replace approach can effectively swap objects in 3D scenes with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of RAM3D by applying it to various realistic 3D scene types, showcasing results of modified objects that blend in seamlessly with the scene without impacting its overall integrity. | ReplaceAnything3D: Text-Guided Object Replacement in 3D Scenes with Compositional Scene Representations | [
"Edward Bartrum",
"Thu Nguyen-Phuoc",
"Chris Xie",
"Zhengqin Li",
"Numair Khan",
"Armen Avetisyan",
"Douglas Lanman",
"Lei Xiao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8HeUvbImKT | @inproceedings{
granz2024weiper,
title={WeiPer: {OOD} Detection using Weight Perturbations of Class Projections},
author={Maximilian Granz and Manuel Heurich and Tim Landgraf},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8HeUvbImKT}
} | Recent advances in out-of-distribution (OOD) detection on image data show that pre-trained neural network classifiers can separate in-distribution (ID) from OOD data well, leveraging the class-discriminative ability of the model itself. Methods have been proposed that either use logit information directly or that process the model's penultimate layer activations. With "WeiPer", we introduce perturbations of the class projections in the final fully connected layer which creates a richer representation of the input. We show that this simple trick can improve the OOD detection performance of a variety of methods and additionally propose a distance-based method that leverages the properties of the augmented WeiPer space. We achieve state-of-the-art OOD detection results across multiple benchmarks of the OpenOOD framework, especially pronounced in difficult settings in which OOD samples are positioned close to the training set distribution. We support our findings with theoretical motivations and empirical observations, and run extensive ablations to provide insights into why WeiPer works. Our code is available at: https://github.com/mgranz/weiper. | WeiPer: OOD Detection using Weight Perturbations of Class Projections | [
"Maximilian Granz",
"Manuel Heurich",
"Tim Landgraf"
] | NeurIPS.cc/2024/Conference | 2405.17164 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8Fxqn1tZM1 | @inproceedings{
kalogeropoulos2024scale,
title={Scale Equivariant Graph Metanetworks},
author={Ioannis Kalogeropoulos and Giorgos Bouritsas and Yannis Panagakis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8Fxqn1tZM1}
} | This paper pertains to an emerging machine learning paradigm: learning higher- order functions, i.e. functions whose inputs are functions themselves, particularly when these inputs are Neural Networks (NNs). With the growing interest in architectures that process NNs, a recurring design principle has permeated the field: adhering to the permutation symmetries arising from the connectionist structure of
NNs. However, are these the sole symmetries present in NN parameterizations? Zooming into most practical activation functions (e.g. sine, ReLU, tanh) answers this question negatively and gives rise to intriguing new symmetries, which we collectively refer to as scaling symmetries, that is, non-zero scalar multiplications and divisions of weights and biases. In this work, we propose Scale Equivariant Graph MetaNetworks - ScaleGMNs, a framework that adapts the Graph Metanetwork (message-passing) paradigm by incorporating scaling symmetries and thus rendering neuron and edge representations equivariant to valid scalings. We introduce novel building blocks, of independent technical interest, that allow for equivariance or invariance with respect to individual scalar multipliers or their product and use them in all components of ScaleGMN. Furthermore, we prove that, under certain expressivity conditions, ScaleGMN can simulate the forward and backward pass of any input feedforward neural network. Experimental results demonstrate that our method advances the state-of-the-art performance for several datasets and activation functions, highlighting the power of scaling symmetries as an inductive bias for NN processing. The source code is publicly available at https://github.com/jkalogero/scalegmn. | Scale Equivariant Graph Metanetworks | [
"Ioannis Kalogeropoulos",
"Giorgos Bouritsas",
"Yannis Panagakis"
] | NeurIPS.cc/2024/Conference | 2406.10685 | [
"https://github.com/jkalogero/scalegmn"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=8Dy42ThoNe | @inproceedings{
yao2024large,
title={Large Language Model Unlearning},
author={Yuanshun Yao and Xiaojun Xu and Yang Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8Dy42ThoNe}
} | We study how to perform unlearning, i.e. forgetting undesirable (mis)behaviors, on large language models (LLMs). We show at least three scenarios of aligning LLMs with human preferences can benefit from unlearning: (1) removing harmful responses, (2) erasing copyright-protected content as requested, and (3) reducing hallucinations. Unlearning, as an alignment technique, has three advantages. (1) It only requires negative (e.g. harmful) examples, which are much easier and cheaper to collect (e.g. via red teaming or user reporting) than positive (e.g. helpful and often human-written) examples required in the standard alignment process. (2) It is computationally efficient. (3) It is especially effective when we know which training samples cause the misbehavior. To the best of our knowledge, our work is among the first to explore LLM unlearning. We are also among the first to formulate the settings, goals, and evaluations in LLM unlearning. Despite only having negative samples, our ablation study shows that unlearning can still achieve better alignment performance than RLHF with just 2% of its computational time. | Large Language Model Unlearning | [
"Yuanshun Yao",
"Xiaojun Xu",
"Yang Liu"
] | NeurIPS.cc/2024/Conference | 2310.10683 | [
"https://github.com/kevinyaobytedance/llm_unlearn"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8Dkz60yGfj | @inproceedings{
ai2024adjust,
title={Adjust Pearson's \$r\$ to Measure Arbitrary Monotone Dependence},
author={Xinbo Ai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8Dkz60yGfj}
} | Pearson's $r$, the most widely-used correlation coefficient, is traditionally regarded as exclusively capturing linear dependence, leading to its discouragement in contexts involving nonlinear relationships. However, recent research challenges this notion, suggesting that Pearson's $r$ should not be ruled out a priori for measuring nonlinear monotone relationships. Pearson's $r$ is essentially a scaled covariance, rooted in the renowned Cauchy-Schwarz Inequality. Our findings reveal that different scaling bounds yield coefficients with different capture ranges, and interestingly, tighter bounds actually expand these ranges. We derive a tighter inequality than Cauchy-Schwarz Inequality, leverage it to refine Pearson's $r$, and propose a new correlation coefficient, i.e., rearrangement correlation. This coefficient is able to capture arbitrary monotone relationships, both linear and nonlinear ones. It reverts to Pearson's $r$ in linear scenarios. Simulation experiments and real-life investigations show that the rearrangement correlation is more accurate in measuring nonlinear monotone dependence than the three classical correlation coefficients, and other recently proposed dependence measures. | Adjust Pearson's r to Measure Arbitrary Monotone Dependence | [
"Xinbo Ai"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/byaxb/recor"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8CguPoe3TP | @inproceedings{
bariletto2024bayesian,
title={Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization},
author={Nicola Bariletto and Nhat Ho},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8CguPoe3TP}
} | Training machine learning and statistical models often involves optimizing a data-driven risk criterion. The risk is usually computed with respect to the empirical data distribution, but this may result in poor and unstable out-of-sample performance due to distributional uncertainty. In the spirit of distributionally robust optimization, we propose a novel robust criterion by combining insights from Bayesian nonparametric (i.e., Dirichlet process) theory and a recent decision-theoretic model of smooth ambiguity-averse preferences. First, we highlight novel connections with standard regularized empirical risk minimization techniques, among which Ridge and LASSO regressions. Then, we theoretically demonstrate the existence of favorable finite-sample and asymptotic statistical guarantees on the performance of the robust optimization procedure. For practical implementation, we propose and study tractable approximations of the criterion based on well-known Dirichlet process representations. We also show that the smoothness of the criterion naturally leads to standard gradient-based numerical optimization. Finally, we provide insights into the workings of our method by applying it to a variety of tasks based on simulated and real datasets. | Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization | [
"Nicola Bariletto",
"Nhat Ho"
] | NeurIPS.cc/2024/Conference | 2401.15771 | [
"https://github.com/nbariletto/bnp_for_dro"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8CBcdDQFDQ | @inproceedings{
fisch2024stratified,
title={Stratified Prediction-Powered Inference for Effective Hybrid Evaluation of Language Models},
author={Adam Fisch and Joshua Maynez and R. Alex Hofer and Bhuwan Dhingra and Amir Globerson and William W. Cohen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8CBcdDQFDQ}
} | Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. PPI achieves this by combining small amounts of human-labeled data with larger amounts of data labeled by a reasonably accurate---but potentially biased---automatic system, in a way that results in tighter confidence intervals for certain parameters of interest (e.g., the mean performance of a language model). In this paper, we propose a method called Stratified Prediction-Powered Inference (StratPPI), in which we show that the basic PPI estimates can be considerably improved by employing simple data stratification strategies. Without making any assumptions on the underlying automatic labeling system or data distribution, we derive an algorithm for computing provably valid confidence intervals for parameters of any dimensionality that is based on stratified sampling. In particular, we show both theoretically and empirically that, with appropriate choices of stratification and sample allocation, our approach can provide substantially tighter confidence intervals than unstratified approaches. Specifically, StratPPI is expected to improve in cases where the performance of the autorater varies across different conditional distributions of the target data. | Stratified Prediction-Powered Inference for Effective Hybrid Evaluation of Language Models | [
"Adam Fisch",
"Joshua Maynez",
"R. Alex Hofer",
"Bhuwan Dhingra",
"Amir Globerson",
"William W. Cohen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8B3sAX889P | @inproceedings{
zhang2024unified,
title={Unified Insights: Harnessing Multi-modal Data for Phenotype Imputation via View Decoupling},
author={Qiannan Zhang and Weishen Pan and Zilong Bai and Chang Su and Fei Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8B3sAX889P}
} | Phenotype imputation plays a crucial role in improving comprehensive and accurate medical evaluation, which in turn can optimize patient treatment and bolster the reliability of clinical research. Despite the adoption of various techniques, multi-modal biological data, which can provide crucial insights into a patient's overall health, is often overlooked. With multi-modal biological data, patient characterization can be enriched from two distinct views: the biological view and the phenotype view. However, the heterogeneity and imprecise nature of the multimodal data still pose challenges in developing an effective method to model from two views. In this paper, we propose a novel framework to incorporate multi-modal biological data via view decoupling. Specifically, we segregate the modeling of biological data from phenotype data in a graph-based learning framework. From the biological view, the latent factors in biological data are discovered to model patient correlation. From the phenotype view, phenotype co-occurrence can be modeled to reveal patterns across patients. Then patients are encoded from these two distinct views. To mitigate the influence of noise and irrelevant information in biological data, we devise a cross-view contrastive knowledge distillation aimed at distilling insights from the biological view to enhance phenotype imputation. We show that phenotype imputation with the proposed model significantly outperforms the state-of-the-art models on the real-world biomedical database. | Unified Insights: Harnessing Multi-modal Data for Phenotype Imputation via View Decoupling | [
"Qiannan Zhang",
"Weishen Pan",
"Zilong Bai",
"Chang Su",
"Fei Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8APPypS0yN | @inproceedings{
pellizzoni2024on,
title={On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks},
author={Paolo Pellizzoni and Till Hendrik Schulz and Dexiong Chen and Karsten Borgwardt},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8APPypS0yN}
} | Graph neural networks (GNNs) employing message passing for graph classification are inherently limited by the expressive power of the Weisfeiler-Leman (WL) test for graph isomorphism. Node individualization schemes, which assign unique identifiers to nodes (e.g., by adding random noise to features), are a common approach for achieving universal expressiveness. However, the ability of GNNs endowed with individualization schemes to generalize beyond the training data is still an open question. To address this question, this paper presents a theoretical analysis of the sample complexity of such GNNs from a statistical learning perspective, employing Vapnik–Chervonenkis (VC) dimension and covering number bounds. We demonstrate that node individualization schemes that are permutation-equivariant result in lower sample complexity, and design novel individualization schemes that exploit these results. As an application of this analysis, we also develop a novel architecture that can perform substructure identification (i.e., subgraph isomorphism) while having a lower VC dimension compared to competing methods. Finally, our theoretical findings are validated experimentally on both synthetic and real-world datasets. | On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks | [
"Paolo Pellizzoni",
"Till Hendrik Schulz",
"Dexiong Chen",
"Karsten Borgwardt"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=89fSR2gpxp | @inproceedings{
lei2024offline,
title={Offline Behavior Distillation},
author={Shiye Lei and Sen Zhang and Dacheng Tao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=89fSR2gpxp}
} | Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity $\mathcal{O}(1/(1-\gamma)^2)$. We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity $\mathcal{O}(1/(1-\gamma))$. Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization. | Offline Behavior Distillation | [
"Shiye Lei",
"Sen Zhang",
"Dacheng Tao"
] | NeurIPS.cc/2024/Conference | 2410.22728 | [
"https://github.com/leaveslei/obd"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=89AUi5L1uA | @inproceedings{
han2024softs,
title={{SOFTS}: Efficient Multivariate Time Series Forecasting with Series-Core Fusion},
author={Lu Han and Xu-Yang Chen and Han-Jia Ye and De-Chuan Zhan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=89AUi5L1uA}
} | Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Recent studies have highlighted the advantages of channel independence to resist distribution drift but neglect channel correlations, limiting further enhancements. Several methods utilize mechanisms like attention or mixer to address this by capturing channel correlations, but they either introduce excessive complexity or rely too heavily on the correlation to achieve satisfactory results under distribution drifts, particularly with a large number of channels. Addressing this gap, this paper presents an efficient MLP-based model, the Series-cOre Fused Time Series forecaster (SOFTS), which incorporates a novel STar Aggregate-Redistribute (STAR) module. Unlike traditional approaches that manage channel interactions through distributed structures, \textit{e.g.}, attention, STAR employs a centralized strategy to improve efficiency and reduce reliance on the quality of each channel. It aggregates all series to form a global core representation, which is then dispatched and fused with individual series representations to facilitate channel interactions effectively. SOFTS achieves superior performance over existing state-of-the-art methods with only linear complexity. The broad applicability of the STAR module across different forecasting models is also demonstrated empirically. We have made our code publicly available at https://github.com/Secilia-Cxy/SOFTS. | SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion | [
"Lu Han",
"Xu-Yang Chen",
"Han-Jia Ye",
"De-Chuan Zhan"
] | NeurIPS.cc/2024/Conference | 2404.14197 | [
"https://github.com/secilia-cxy/softs"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=88rbNOtAez | @inproceedings{
fang2024makeitreal,
title={Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials},
author={Ye Fang and Zeyi Sun and Tong Wu and Jiaqi Wang and Ziwei Liu and Gordon Wetzstein and Dahua Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=88rbNOtAez}
} | Physically realistic materials are pivotal in augmenting the realism of 3D assets across various applications and lighting conditions. However, existing 3D assets and generative models often lack authentic material properties. Manual assignment of materials using graphic software is a tedious and time-consuming task. In this paper, we exploit advancements in Multimodal Large Language Models (MLLMs), particularly GPT-4V, to present a novel approach, Make-it-Real: 1) We demonstrate that GPT-4V can effectively recognize and describe materials, allowing the construction of a detailed material library. 2) Utilizing a combination of visual cues and hierarchical text prompts, GPT-4V precisely identifies and aligns materials with the corresponding components of 3D objects. 3) The correctly matched materials are then meticulously applied as reference for the new SVBRDF material generation according to the original albedo map, significantly enhancing their visual authenticity. Make-it-Real offers a streamlined integration into the 3D content creation workflow, showcasing its utility as an essential tool for developers of 3D assets. | Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials | [
"Ye Fang",
"Zeyi Sun",
"Tong Wu",
"Jiaqi Wang",
"Ziwei Liu",
"Gordon Wetzstein",
"Dahua Lin"
] | NeurIPS.cc/2024/Conference | 2404.16829 | [
""
] | https://huggingface.co/papers/2404.16829 | 3 | 5 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=88TzdGyPT6 | @inproceedings{
karhadkar2024benign,
title={Benign overfitting in leaky Re{LU} networks with moderate input dimension},
author={Kedar Karhadkar and Erin George and Michael Murray and Guido Montufar and Deanna Needell},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=88TzdGyPT6}
} | The problem of benign overfitting asks whether it is possible for a model to perfectly fit noisy training data and still generalize well. We study benign overfitting in two-layer leaky ReLU networks trained with the hinge loss on a binary classification task. We consider input data which can be decomposed into the sum of a common signal and a random noise component, which lie on subspaces orthogonal to one another. We characterize conditions on the signal to noise ratio (SNR) of the model parameters giving rise to benign versus non-benign, or harmful, overfitting: in particular, if the SNR is high then benign overfitting occurs, conversely if the SNR is low then harmful overfitting occurs. We attribute both benign and non-benign overfitting to an approximate margin maximization property and show that leaky ReLU networks trained on hinge loss with gradient descent (GD) satisfy this property. In contrast to prior work we do not require the training data to be nearly orthogonal. Notably, for input dimension $d$ and training sample size $n$, while results in prior work require $d = \Omega(n^2 \log n)$, here we require only $d = \Omega(n)$. | Benign overfitting in leaky ReLU networks with moderate input dimension | [
"Kedar Karhadkar",
"Erin George",
"Michael Murray",
"Guido Montufar",
"Deanna Needell"
] | NeurIPS.cc/2024/Conference | 2403.06903 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=87AXdbkRyd | @inproceedings{
yu2024selfsupervised,
title={Self-supervised Transformation Learning for Equivariant Representations},
author={Jaemyung Yu and Jaehyun Choi and Dong-Jae Lee and HyeongGwon Hong and Junmo Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=87AXdbkRyd}
} | Unsupervised representation learning has significantly advanced various machine learning tasks. In the computer vision domain, state-of-the-art approaches utilize transformations like random crop and color jitter to achieve invariant representations, embedding semantically the same inputs despite transformations. However, this can degrade performance in tasks requiring precise features, such as localization or flower classification. To address this, recent research incorporates equivariant representation learning, which captures transformation-sensitive information. However, current methods depend on transformation labels and thus struggle with interdependency and complex transformations. We propose Self-supervised Transformation Learning (STL), replacing transformation labels with transformation representations derived from image pairs. The proposed method ensures transformation representation is image-invariant and learns corresponding equivariant transformations, enhancing performance without increased batch complexity. We demonstrate the approach’s effectiveness across diverse classification and detection tasks, outperforming existing methods in 7 out of 11 benchmarks and excelling in detection. By integrating complex transformations like AugMix, unusable by prior equivariant methods, this approach enhances performance across tasks, underscoring its adaptability and resilience. Additionally, its compatibility with various base models highlights its flexibility and broad applicability. The code is available at https://github.com/jaemyung-u/stl. | Self-supervised Transformation Learning for Equivariant Representations | [
"Jaemyung Yu",
"Jaehyun Choi",
"Dong-Jae Lee",
"HyeongGwon Hong",
"Junmo Kim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=85tu7K06i3 | @inproceedings{
cohen2024looks,
title={Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models},
author={Regev Cohen and Idan Kligvasser and Ehud Rivlin and Daniel Freedman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=85tu7K06i3}
} | The pursuit of high perceptual quality in image restoration has driven the development of revolutionary generative models, capable of producing results often visually indistinguishable from real data.
However, as their perceptual quality continues to improve, these models also exhibit a growing tendency to generate hallucinations – realistic-looking details that do not exist in the ground truth images.
Hallucinations in these models create uncertainty about their reliability, raising major concerns about their practical application.
This paper investigates this phenomenon through the lens of information theory, revealing a fundamental tradeoff between uncertainty and perception. We rigorously analyze the relationship between these two factors, proving that the global minimal uncertainty in generative models grows in tandem with perception.
In particular, we define the inherent uncertainty of the restoration problem and show that attaining perfect perceptual quality entails at least twice this uncertainty. Additionally, we establish a relation between distortion, uncertainty and perception, through which we prove the aforementioned uncertainly-perception tradeoff induces the well-known perception-distortion tradeoff.
We demonstrate our theoretical findings through experiments with super-resolution and inpainting algorithms.
This work uncovers fundamental limitations of generative models in achieving both high perceptual quality and reliable predictions for image restoration.
Thus, we aim to raise awareness among practitioners about this inherent tradeoff, empowering them to make informed decisions and potentially prioritize safety over perceptual performance. | Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models | [
"Regev Cohen",
"Idan Kligvasser",
"Ehud Rivlin",
"Daniel Freedman"
] | NeurIPS.cc/2024/Conference | 2405.16475 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=859DtlwnAD | @inproceedings{
wang2024pintuning,
title={Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction},
author={Liang Wang and Qiang Liu and Shaozhen Liu and Xin Sun and Shu Wu and Liang Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=859DtlwnAD}
} | Molecular property prediction (MPP) is integral to drug discovery and material science, but often faces the challenge of data scarcity in real-world scenarios. Addressing this, few-shot molecular property prediction (FSMPP) has been developed. Unlike other few-shot tasks, FSMPP typically employs a pre-trained molecular encoder and a context-aware classifier, benefiting from molecular pre-training and molecular context information. Despite these advancements, existing methods struggle with the ineffective fine-tuning of pre-trained encoders. We attribute this issue to the imbalance between the abundance of tunable parameters and the scarcity of labeled molecules, and the lack of contextual perceptiveness in the encoders. To overcome this hurdle, we propose a parameter-efficient in-context tuning method, named Pin-Tuning. Specifically, we propose a lightweight adapter for pre-trained message passing layers (MP-Adapter) and Bayesian weight consolidation for pre-trained atom/bond embedding layers (Emb-BWC), to achieve parameter-efficient tuning while preventing over-fitting and catastrophic forgetting. Additionally, we enhance the MP-Adapters with contextual perceptiveness. This innovation allows for in-context tuning of the pre-trained encoder, thereby improving its adaptability for specific FSMPP tasks. When evaluated on public datasets, our method demonstrates superior tuning with fewer trainable parameters, improving few-shot predictive performance. | Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction | [
"Liang Wang",
"Qiang Liu",
"Shaozhen Liu",
"Xin Sun",
"Shu Wu",
"Liang Wang"
] | NeurIPS.cc/2024/Conference | 2411.01158 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=848vuK2cKp | @inproceedings{
qian2024offline,
title={Offline Oracle-Efficient Learning for Contextual {MDP}s via Layerwise Exploration-Exploitation Tradeoff},
author={Jian Qian and Haichen Hu and David Simchi-Levi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=848vuK2cKp}
} | Motivated by the recent discovery of a statistical and computational reduction from contextual bandits to offline regression \citep{simchi2020bypassing}, we address the general (stochastic) Contextual Markov Decision Process (CMDP) problem with horizon $H$ (as known as CMDP with $H$ layers). In this paper, we introduce a reduction from CMDPs to offline density estimation under the realizability assumption, i.e., a model class $\mathcal{M}$ containing the true underlying CMDP is provided in advance. We develop an efficient, statistically near-optimal algorithm requiring only $O(H \log T)$ calls to an offline density estimation algorithm (or oracle) across all $T$ rounds. This number can be further reduced to $O(H \log \log T)$ if $T$ is known in advance. Our results mark the first efficient and near-optimal reduction from CMDPs to offline density estimation without imposing any structural assumptions on the model class. A notable feature of our algorithm is the design of a layerwise exploration-exploitation tradeoff tailored to address the layerwise structure of CMDPs. Additionally, our algorithm is versatile and applicable to pure exploration tasks in reward-free reinforcement learning. | Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff | [
"Jian Qian",
"Haichen Hu",
"David Simchi-Levi"
] | NeurIPS.cc/2024/Conference | 2405.17796 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=83vxe8alV4 | @inproceedings{
cinquin2024fsplaplace,
title={{FSP}-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning},
author={Tristan Cinquin and Marvin Pf{\"o}rtner and Vincent Fortuin and Philipp Hennig and Robert Bamler},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=83vxe8alV4}
} | Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained network, and they scale to large models and datasets. While the choice of prior strongly affects the resulting posterior distribution, computational tractability and lack of interpretability of the weight space typically limit the Laplace approximation to isotropic Gaussian priors, which are known to cause pathological behavior as depth increases. As a remedy, we directly place a prior on function space. More precisely, since Lebesgue densities do not exist on infinite-dimensional function spaces, we recast training as finding the so-called weak mode of the posterior measure under a Gaussian process (GP) prior restricted to the space of functions representable by the neural network. Through the GP prior, one can express structured and interpretable inductive biases, such as regularity or periodicity, directly in function space, while still exploiting the implicit inductive biases that allow deep networks to generalize. After model linearization, the training objective induces a negative log-posterior density to which we apply a Laplace approximation, leveraging highly scalable methods from matrix-free linear algebra. Our method provides improved results where prior knowledge is abundant (as is the case in many scientific inference tasks). At the same time, it stays competitive for black-box supervised learning problems, where neural networks typically excel. | FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning | [
"Tristan Cinquin",
"Marvin Pförtner",
"Vincent Fortuin",
"Philipp Hennig",
"Robert Bamler"
] | NeurIPS.cc/2024/Conference | 2407.13711 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=83pV20DD2s | @inproceedings{
chen2024learning,
title={Learning from Pattern Completion: Self-supervised Controllable Generation},
author={Zhiqiang Chen and Guofan Fan and Jinying Gao and Lei Ma and Bo Lei and Tiejun Huang and Shan Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=83pV20DD2s}
} | The human brain exhibits a strong ability to spontaneously associate different visual attributes of the same or similar visual scene, such as associating sketches and graffiti with real-world visual objects, usually without supervising information. In contrast, in the field of artificial intelligence, controllable generation methods like ControlNet heavily rely on annotated training datasets such as depth maps, semantic segmentation maps, and poses, which limits the method’s scalability. Inspired by the neural mechanisms that may contribute to the brain’s associative power, specifically the cortical modularization and hippocampal pattern completion, here we propose a self-supervised controllable generation (SCG) framework. Firstly, we introduce an equivariance constraint to promote inter-module independence and intra-module correlation in a modular autoencoder network, thereby achieving functional specialization. Subsequently, based on these specialized modules, we employ a self-supervised pattern completion approach for controllable generation training. Experimental results demonstrate that the proposed modular autoencoder effectively achieves functional specialization, including the modular processing of color, brightness, and edge detection, and exhibits brain-like features including orientation selectivity, color antagonism, and center-surround receptive fields. Through self-supervised training, associative generation capabilities spontaneously emerge in SCG, demonstrating excellent zero-shot generalization ability to various tasks such as superresolution, dehaze and associative or conditional generation on painting, sketches, and ancient graffiti. Compared to the previous representative method ControlNet, our proposed approach not only demonstrates superior robustness in more challenging high-noise scenarios but also possesses more promising scalability potential due to its self-supervised manner. Codes are released on Github and Gitee. | Learning from Pattern Completion: Self-supervised Controllable Generation | [
"Zhiqiang Chen",
"Guofan Fan",
"Jinying Gao",
"Lei Ma",
"Bo Lei",
"Tiejun Huang",
"Shan Yu"
] | NeurIPS.cc/2024/Conference | 2409.18694 | [
"https://github.com/BAAI-Brain-Inspired-Group/OPEN-Vis-ControlSD"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=83e3DPVrFC | @inproceedings{
cheng2024rethinking,
title={Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation},
author={Jiaxin Cheng and Zixu Zhao and Tong He and Tianjun Xiao and Yicong Zhou and Zheng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=83e3DPVrFC}
} | Recent advancements in generative models have significantly enhanced their capacity for image generation, enabling a wide range of applications such as image editing, completion and video editing. A specialized area within generative modeling is layout-to-image (L2I) generation, where predefined layouts of objects guide the generative process. In this study, we introduce a novel regional cross-attention module tailored to enrich layout-to-image generation. This module notably improves the representation of layout regions, particularly in scenarios where existing methods struggle with highly complex and detailed textual descriptions. Moreover, while current open-vocabulary L2I methods are trained in an open-set setting, their evaluations often occur in closed-set environments. To bridge this gap, we propose two metrics to assess L2I performance in open-vocabulary scenarios. Additionally, we conduct a comprehensive user study to validate the consistency of these metrics with human preferences. | Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation | [
"Jiaxin Cheng",
"Zixu Zhao",
"Tong He",
"Tianjun Xiao",
"Yicong Zhou",
"Zheng Zhang"
] | NeurIPS.cc/2024/Conference | 2409.04847 | [
"https://github.com/cplusx/rich_context_l2i"
] | https://huggingface.co/papers/2409.04847 | 0 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=82Ndsr4OS6 | @inproceedings{
wei2024adversarially,
title={Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning},
author={Honghao Wei and Xiyue Peng and Arnob Ghosh and Xin Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=82Ndsr4OS6}
} | We propose WSAC (Weighted Safe Actor-Critic), a novel algorithm for Safe Offline Reinforcement Learning (RL) under functional approximation, which can robustly optimize policies to improve upon an arbitrary reference policy with limited data coverage. WSAC is designed as a two-player Stackelberg game to optimize a refined objective function. The actor optimizes the policy against two adversarially trained value critics with small importance-weighted Bellman errors, which focus on scenarios where the actor's performance is inferior to the reference policy. In theory, we demonstrate that when the actor employs a no-regret optimization oracle, WSAC achieves a number of guarantees: $(i)$ For the first time in the safe offline RL setting, we establish that WSAC can produce a policy that outperforms {\bf any} reference policy while maintaining the same level of safety, which is critical to designing a safe algorithm for offline RL. $(ii)$ WSAC achieves the optimal statistical convergence rate of $1/\sqrt{N}$ to the reference policy, where $N$ is the size of the offline dataset. $(iii)$ We theoretically show that WSAC guarantees a safe policy improvement across a broad range of hyperparameters that control the degree of pessimism, indicating its practical robustness. Additionally, we offer a practical version of WSAC and compare it with existing state-of-the-art safe offline RL algorithms in several continuous control environments. WSAC outperforms all baselines across a range of tasks, supporting the theoretical results. | Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning | [
"Honghao Wei",
"Xiyue Peng",
"Arnob Ghosh",
"Xin Liu"
] | NeurIPS.cc/2024/Conference | 2401.00629 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8271eFxojN | @inproceedings{
wang2024identifiability,
title={Identifiability Analysis of Linear {ODE} Systems with Hidden Confounders},
author={Yuanyuan Wang and Biwei Huang and Wei Huang and Xi Geng and Mingming Gong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8271eFxojN}
} | The identifiability analysis of linear Ordinary Differential Equation (ODE) systems is a necessary prerequisite for making reliable causal inferences about these systems. While identifiability has been well studied in scenarios where the system is fully observable, the conditions for identifiability remain unexplored when latent variables interact with the system. This paper aims to address this gap by presenting a systematic analysis of identifiability in linear ODE systems incorporating hidden confounders. Specifically, we investigate two cases of such systems. In the first case, latent confounders exhibit no causal relationships, yet their evolution adheres to specific functional forms, such as polynomial functions of time $t$. Subsequently, we extend this analysis to encompass scenarios where hidden confounders exhibit causal dependencies, with the causal structure of latent variables described by a Directed Acyclic Graph (DAG). The second case represents a more intricate variation of the first case, prompting a more comprehensive identifiability analysis. Accordingly, we conduct detailed identifiability analyses of the second system under various observation conditions, including both continuous and discrete observations from single or multiple trajectories. To validate our theoretical results, we perform a series of simulations, which support and substantiate our findings. | Identifiability Analysis of Linear ODE Systems with Hidden Confounders | [
"Yuanyuan Wang",
"Biwei Huang",
"Wei Huang",
"Xi Geng",
"Mingming Gong"
] | NeurIPS.cc/2024/Conference | 2410.21917 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=81YIt63TTn | @inproceedings{
lu2024twinmerging,
title={Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging},
author={Zhenyi Lu and Chenghao Fan and Wei Wei and Xiaoye Qu and Dangyang Chen and Yu Cheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=81YIt63TTn}
} | In the era of large language models, model merging is a promising way to combine multiple task-specific models into a single multitask model without extra training.
However, two challenges remain: (a) interference between different models and (b) heterogeneous data during testing. Traditional model merging methods often show significant performance gaps compared to fine-tuned models due to these issues.
Additionally, a one-size-fits-all model lacks flexibility for diverse test data, leading to performance degradation.
We show that both shared and exclusive task-specific knowledge are crucial for merging performance, but directly merging exclusive knowledge hinders overall performance.
In view of this, we propose Twin-Merging, a method that encompasses two principal stages:
(1) modularizing knowledge into shared and exclusive components, with compression to reduce redundancy and enhance efficiency;
(2) dynamically merging shared and task-specific knowledge based on the input.
This approach narrows the performance gap between merged and fine-tuned models and improves adaptability to heterogeneous data.
Extensive experiments on $20$ datasets for both language and vision tasks demonstrate the effectiveness of our method, showing an average improvement of $28.34\%$ in absolute normalized score for discriminative tasks and even surpassing the fine-tuned upper bound on the generative tasks. | Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging | [
"Zhenyi Lu",
"Chenghao Fan",
"Wei Wei",
"Xiaoye Qu",
"Dangyang Chen",
"Yu Cheng"
] | NeurIPS.cc/2024/Conference | 2406.15479 | [
"https://github.com/LZY-the-boys/Twin-Merging"
] | https://huggingface.co/papers/2406.15479 | 4 | 2 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=81IFFsfQUj | @inproceedings{
wang2024dmplug,
title={{DMP}lug: A Plug-in Method for Solving Inverse Problems with Diffusion Models},
author={Hengkang Wang and Xu Zhang and Taihui Li and Yuxiang Wan and Tiancong Chen and Ju Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=81IFFsfQUj}
} | Pretrained diffusion models (DMs) have recently been popularly used in solving inverse problems (IPs). The existing methods mostly interleave iterative steps in the reverse diffusion process and iterative steps to bring the iterates closer to satisfying the measurement constraint. However, such interleaving methods struggle to produce final results that look like natural objects of interest (i.e., manifold feasibility) and fit the measurement (i.e., measurement feasibility), especially for nonlinear IPs. Moreover, their capabilities to deal with noisy IPs with unknown types and levels of measurement noise are unknown. In this paper, we advocate viewing the reverse process in DMs as a function and propose a novel plug-in method for solving IPs using pretrained DMs, dubbed DMPlug. DMPlug addresses the issues of manifold feasibility and measurement feasibility in a principled manner, and also shows great potential for being robust to unknown types and levels of noise. Through extensive experiments across various IP tasks, including two linear and three nonlinear IPs, we demonstrate that DMPlug consistently outperforms state-of-the-art methods, often by large margins especially for nonlinear IPs. | DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models | [
"Hengkang Wang",
"Xu Zhang",
"Taihui Li",
"Yuxiang Wan",
"Tiancong Chen",
"Ju Sun"
] | NeurIPS.cc/2024/Conference | 2405.16749 | [
"https://github.com/sun-umn/dmplug"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=80SSl69GAz | @inproceedings{
csord{\'a}s2024switchhead,
title={SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention},
author={R{\'o}bert Csord{\'a}s and Piotr Pi{\k{e}}kos and Kazuki Irie and J{\"u}rgen Schmidhuber},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=80SSl69GAz}
} | Despite many recent works on Mixture of Experts (MoEs) for resource-efficient Transformer language models, existing methods mostly focus on MoEs for feedforward layers. Previous attempts at extending MoE to the self-attention layer fail to match the performance of the parameter-matched baseline. Our novel SwitchHead is an effective MoE method for the attention layer that successfully reduces both the compute and memory requirements, achieving wall-clock speedup, while matching the language modeling performance of the baseline Transformer. Our novel MoE mechanism allows SwitchHead to compute up to 8 times fewer attention matrices than the standard Transformer. SwitchHead can also be combined with MoE feedforward layers, resulting in fully-MoE "SwitchAll" Transformers. For our 262M parameter model trained on C4, SwitchHead matches the perplexity of standard models with only 44% compute and 27% memory usage. Zero-shot experiments on downstream tasks confirm the performance of SwitchHead, e.g., achieving more than 3.5% absolute improvements on BliMP compared to the baseline with an equal compute resource. | SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention | [
"Róbert Csordás",
"Piotr Piękos",
"Kazuki Irie",
"Jürgen Schmidhuber"
] | NeurIPS.cc/2024/Conference | 2312.07987 | [
"https://github.com/robertcsordas/moe_attention"
] | https://huggingface.co/papers/2312.07987 | 1 | 40 | 2 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=7zzOcyT0hd | @inproceedings{
poiani2024suboptimal,
title={Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning},
author={Riccardo Poiani and Curti Gabriele and Alberto Maria Metelli and Marcello Restelli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7zzOcyT0hd}
} | Inverse Reinforcement Learning (IRL) deals with the problem of deducing a reward function that explains the behavior of an expert agent who is assumed to act *optimally* in an underlying unknown task. Recent works have studied the IRL problem from the perspective of recovering the *feasible reward set*, i.e., the class of reward functions that are compatible with a unique optimal expert. However, in several problems of interest it is possible to observe the behavior of multiple experts with different degree of optimality (e.g., racing drivers whose skills ranges from amateurs to professionals). For this reason, in this work, we focus on the reconstruction of the feasible reward set when, in addition to demonstrations from the optimal expert, we observe the behavior of multiple *sub-optimal experts*. Given this problem, we first study the theoretical properties showing that the presence of multiple sub-optimal experts, in addition to the optimal one, can significantly shrink the set of compatible rewards, ultimately mitigating the inherent ambiguity of IRL.
Furthermore, we study the statistical complexity of estimating the feasible reward set with a generative model and analyze a uniform sampling algorithm that turns out to be minimax optimal whenever the sub-optimal experts' performance level is sufficiently close to that of the optimal expert. | Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning | [
"Riccardo Poiani",
"Curti Gabriele",
"Alberto Maria Metelli",
"Marcello Restelli"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=7xhwE7VH4S | @inproceedings{
girish2024queen,
title={{QUEEN}: {QU}antized Efficient {EN}coding for Streaming Free-viewpoint Videos},
author={Sharath Girish and Tianye Li and Amrita Mazumdar and Abhinav Shrivastava and david luebke and Shalini De Mello},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7xhwE7VH4S}
} | Online free-viewpoint video (FVV) streaming is a challenging problem, which is relatively under-explored. It requires incremental on-the-fly updates to a volumetric representation, fast training and rendering to satisfy realtime constraints and a small memory footprint for efficient transmission. If acheived, it can enhance user experience by enabling novel applications, e.g., 3D video conferencing and live volumetric video broadcast, among others. In this work, we propose a novel framework for QUantized and Efficient ENcoding (QUEEN) for streaming FVV using 3D Gaussian Splatting (3D-GS). QUEEN directly learns Gaussian attribute residuals between consecutive frames at each time-step without imposing any structural constraints on them, allowing for high quality reconstruction and generalizability. To efficiently store the residuals, we further propose a quantization-sparsity framework, which contains a learned latent-decoder for effectively quantizing attribute residuals other than Gaussian positions and a learned gating module to sparsify position residuals. We propose to use the Gaussian viewspace gradient difference vector as a signal to separate the static and dynamic content of the scene. It acts as a guide for effective sparsity learning and speeds up training. On diverse FVV benchmarks, QUEEN outperforms the state-of-the-art online FVV methods on all metrics. Notably, for several highly dynamic scenes, it reduces the model size to just 0.7 MB per frame while training in under 5 sec and rendering at ~350 FPS. | QUEEN: QUantized Efficient ENcoding for Streaming Free-viewpoint Videos | [
"Sharath Girish",
"Tianye Li",
"Amrita Mazumdar",
"Abhinav Shrivastava",
"david luebke",
"Shalini De Mello"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=7vsx6PxAOH | @inproceedings{
zhang2024allinone,
title={All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation},
author={Xu Zhang and Peiyao Guo and Ming Lu and Zhan Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7vsx6PxAOH}
} | Image coding for multi-task applications, catering to both human perception and machine vision, has been extensively investigated. Existing methods often rely on multiple task-specific encoder-decoder pairs, leading to high overhead of parameter and bitrate usage, or face challenges in multi-objective optimization under a unified representation, failing to achieve both performance and efficiency. To this end, we propose Multi-Path Aggregation (MPA) integrated into existing coding models for joint human-machine vision, unifying the feature representation with an all-in-one architecture. MPA employs a predictor to allocate latent features among task-specific paths based on feature importance varied across tasks, maximizing the utility of shared features while preserving task-specific features for subsequent refinement. Leveraging feature correlations, we develop a two-stage optimization strategy to alleviate multi-task performance degradation. Upon the reuse of shared features, as low as 1.89\% parameters are further augmented and fine-tuned for a specific task, which completely avoids extensive optimization of the entire model. Experimental results show that MPA achieves performance comparable to state-of-the-art methods in both task-specific and multi-objective optimization across human viewing and machine analysis tasks. Moreover, our all-in-one design supports seamless transitions between human- and machine-oriented reconstruction, enabling task-controllable interpretation without altering the unified model. Code is available at https://github.com/NJUVISION/MPA. | All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation | [
"Xu Zhang",
"Peiyao Guo",
"Ming Lu",
"Zhan Ma"
] | NeurIPS.cc/2024/Conference | 2409.19660 | [
"https://github.com/NJUVISION/MPA"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7vXufiEzSy | @inproceedings{
shin2024selfguided,
title={Self-Guided Masked Autoencoder},
author={Jeongwoo Shin and Inseo Lee and Junho Lee and Joonseok Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7vXufiEzSy}
} | Masked Autoencoder (MAE) is a self-supervised approach for representation learning, widely applicable to a variety of downstream tasks in computer vision. In spite of its success, it is still not fully uncovered what and how MAE exactly learns. In this paper, with an in-depth analysis, we discover that MAE intrinsically learns pattern-based patch-level clustering from surprisingly early stages of pre-training. Upon this understanding, we propose self-guided masked autoencoder, which internally generates informed mask by utilizing its progress in patch clustering, substituting the naive random masking of the vanilla MAE. Our approach significantly boosts its learning process without relying on any external models or supplementary information, keeping the benefit of self-supervised nature of MAE intact. Comprehensive experiments on various downstream tasks verify the effectiveness of the proposed method. | Self-Guided Masked Autoencoder | [
"Jeongwoo Shin",
"Inseo Lee",
"Junho Lee",
"Joonseok Lee"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/johnathan-xie/sma"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=7v88Fh6iSM | @inproceedings{
rozet2024learning,
title={Learning Diffusion Priors from Observations by Expectation Maximization},
author={Fran{\c{c}}ois Rozet and G{\'e}r{\^o}me Andry and Francois Lanusse and Gilles Louppe},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7v88Fh6iSM}
} | Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some settings. In this work, we present a novel method based on the expectation-maximization algorithm for training diffusion models from incomplete and noisy observations only. Unlike previous works, our method leads to proper diffusion models, which is crucial for downstream tasks. As part of our method, we propose and motivate an improved posterior sampling scheme for unconditional diffusion models. We present empirical evidence supporting the effectiveness of our method. | Learning Diffusion Priors from Observations by Expectation Maximization | [
"François Rozet",
"Gérôme Andry",
"Francois Lanusse",
"Gilles Louppe"
] | NeurIPS.cc/2024/Conference | 2405.13712 | [
"https://github.com/francois-rozet/diffusion-priors"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7v0UyO0B6q | @inproceedings{
kveton2024online,
title={Online Posterior Sampling with a Diffusion Prior},
author={Branislav Kveton and Boris N. Oreshkin and Youngsuk Park and Aniket Anand Deshmukh and Rui Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7v0UyO0B6q}
} | Posterior sampling in contextual bandits with a Gaussian prior can be implemented exactly or approximately using the Laplace approximation. The Gaussian prior is computationally efficient but it cannot describe complex distributions. In this work, we propose approximate posterior sampling algorithms for contextual bandits with a diffusion model prior. The key idea is to sample from a chain of approximate conditional posteriors, one for each stage of the reverse diffusion process, which are obtained by the Laplace approximation. Our approximations are motivated by posterior sampling with a Gaussian prior, and inherit its simplicity and efficiency. They are asymptotically consistent and perform well empirically on a variety of contextual bandit problems. | Online Posterior Sampling with a Diffusion Prior | [
"Branislav Kveton",
"Boris N. Oreshkin",
"Youngsuk Park",
"Aniket Anand Deshmukh",
"Rui Song"
] | NeurIPS.cc/2024/Conference | 2410.03919 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7uqVfZW6Mo | @inproceedings{
meng2024not,
title={Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features},
author={Benyuan Meng and Qianqian Xu and Zitai Wang and Xiaochun Cao and Qingming Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7uqVfZW6Mo}
} | Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at https://github.com/Darkbblue/generic-diffusion-feature. | Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features | [
"Benyuan Meng",
"Qianqian Xu",
"Zitai Wang",
"Xiaochun Cao",
"Qingming Huang"
] | NeurIPS.cc/2024/Conference | 2410.03558 | [
"https://github.com/darkbblue/generic-diffusion-feature"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=7uWzoGn4kv | @inproceedings{
vo2024henasy,
title={{HENASY}: Learning to Assemble Scene-Entities for Interpretable Egocentric Video-Language Model},
author={Khoa Vo and Thinh Phan and Kashu Yamazaki and Minh Tran and Ngan Hoang Le},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7uWzoGn4kv}
} | Current video-language models (VLMs) rely extensively on instance-level alignment between video and language modalities, which presents two major limitations: (1) visual reasoning disobeys the natural perception that humans do in first-person perspective, leading to a lack of reasoning interpretation; and (2) learning is limited in capturing inherent fine-grained relationships between two modalities.
In this paper, we take an inspiration from human perception and explore a compositional approach for egocentric video representation. We introduce HENASY (Hierarchical ENtities ASsemblY), which includes a spatiotemporal token grouping mechanism to explicitly assemble dynamically evolving scene entities through time and model their relationship for video representation. By leveraging compositional structure understanding, HENASY possesses strong interpretability via visual grounding with free-form text queries. We further explore a suite of multi-grained contrastive losses to facilitate entity-centric understandings. This comprises three alignment types: video-narration, noun-entity, verb-entities alignments.
Our method demonstrates strong interpretability in both quantitative and qualitative experiments; while maintaining competitive performances on five downstream tasks via zero-shot transfer or as video/text representation, including video/text retrieval, action recognition, multi-choice query, natural language query, and moments query.
Project page: https://uark-aicv.github.io/HENASY | HENASY: Learning to Assemble Scene-Entities for Interpretable Egocentric Video-Language Model | [
"Khoa Vo",
"Thinh Phan",
"Kashu Yamazaki",
"Minh Tran",
"Ngan Hoang Le"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=7txPaUpUnc | @inproceedings{
braun2024identifying,
title={Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning},
author={Dan Braun and Jordan Taylor and Nicholas Goldowsky-Dill and Lee Sharkey},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7txPaUpUnc}
} | Identifying the features learned by neural networks is a core challenge in mechanistic interpretability. Sparse autoencoders (SAEs), which learn a sparse, overcomplete dictionary that reconstructs a network's internal activations, have been used to identify these features. However, SAEs may learn more about the structure of the datatset than the computational structure of the network. There is therefore only indirect reason to believe that the directions found in these dictionaries are functionally important to the network. We propose end-to-end (e2e) sparse dictionary learning, a method for training SAEs that ensures the features learned are functionally important by minimizing the KL divergence between the output distributions of the original model and the model with SAE activations inserted. Compared to standard SAEs, e2e SAEs offer a Pareto improvement: They explain more network performance, require fewer total features, and require fewer simultaneously active features per datapoint, all with no cost to interpretability. We explore geometric and qualitative differences between e2e SAE features and standard SAE features. E2e dictionary learning brings us closer to methods that can explain network behavior concisely and accurately. We release our library for training e2e SAEs and reproducing our analysis at
https://github.com/ApolloResearch/e2e_sae. | Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning | [
"Dan Braun",
"Jordan Taylor",
"Nicholas Goldowsky-Dill",
"Lee Sharkey"
] | NeurIPS.cc/2024/Conference | 2405.12241 | [
"https://github.com/apolloresearch/e2e_sae"
] | https://huggingface.co/papers/2405.12241 | 0 | 1 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=7tRtH0AoBl | @inproceedings{
cho2024randomized,
title={Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation},
author={Wooseong Cho and Taehyun Hwang and Joongkyu Lee and Min-hwan Oh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7tRtH0AoBl}
} | We study reinforcement learning with _multinomial logistic_ (MNL) function approximation where the underlying transition probability kernel of the _Markov decision processes_ (MDPs) is parametrized by an unknown transition core with features of state and action. For the finite horizon episodic setting with inhomogeneous state transitions, we propose provably efficient algorithms with randomized exploration having frequentist regret guarantees. For our first algorithm, $\texttt{RRL-MNL}$, we adapt optimistic sampling to ensure the optimism of the estimated value function with sufficient frequency and establish that $\texttt{RRL-MNL}$ is both _statistically_ and _computationally_ efficient, achieving a $\tilde{\mathcal{O}}(\kappa^{-1} d^{\frac{3}{2}} H^{\frac{3}{2}} \sqrt{T})$ frequentist regret bound with constant-time computational cost per episode. Here, $d$ is the dimension of the transition core, $H$ is the horizon length, $T$ is the total number of steps, and $\kappa$ is a problem-dependent constant. Despite the simplicity and practicality of $\texttt{RRL-MNL}$, its regret bound scales with $\kappa^{-1}$, which is potentially large in the worst case. To improve the dependence on $\kappa^{-1}$, we propose $\texttt{ORRL-MNL}$, which estimates the value function using local gradient information of the MNL transition model. We show that its frequentist regret bound is $\tilde{\mathcal{O}}(d^{\frac{3}{2}} H^{\frac{3}{2}} \sqrt{T} + \kappa^{-1} d^2 H^2)$. To the best of our knowledge, these are the first randomized RL algorithms for the MNL transition model that achieve both computational and statistical efficiency. Numerical experiments demonstrate the superior performance of the proposed algorithms. | Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation | [
"Wooseong Cho",
"Taehyun Hwang",
"Joongkyu Lee",
"Min-hwan Oh"
] | NeurIPS.cc/2024/Conference | 2405.20165 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7t9eDEY2GT | @inproceedings{
shen2024flippingbased,
title={Flipping-based Policy for Chance-Constrained Markov Decision Processes},
author={Xun Shen and Shuo Jiang and Akifumi Wachi and Kazumune Hashimoto and Sebastien Gros},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7t9eDEY2GT}
} | Safe reinforcement learning (RL) is a promising approach for many real-world decision-making problems where ensuring safety is a critical necessity. In safe RL research, while expected cumulative safety constraints (ECSCs) are typically the first choices, chance constraints are often more pragmatic for incorporating safety under uncertainties. This paper proposes a \textit{flipping-based policy} for Chance-Constrained Markov Decision Processes (CCMDPs). The flipping-based policy selects the next action by tossing a potentially distorted coin between two action candidates. The probability of the flip and the two action candidates vary depending on the state. We establish a Bellman equation for CCMDPs and further prove the existence of a flipping-based policy within the optimal solution sets. Since solving the problem with joint chance constraints is challenging in practice, we then prove that joint chance constraints can be approximated into Expected Cumulative Safety Constraints (ECSCs) and that there exists a flipping-based policy in the optimal solution sets for constrained MDPs with ECSCs. As a specific instance of practical implementations, we present a framework for adapting constrained policy optimization to train a flipping-based policy. This framework can be applied to other safe RL algorithms. We demonstrate that the flipping-based policy can improve the performance of the existing safe RL algorithms under the same limits of safety constraints on Safety Gym benchmarks. | Flipping-based Policy for Chance-Constrained Markov Decision Processes | [
"Xun Shen",
"Shuo Jiang",
"Akifumi Wachi",
"Kazumune Hashimoto",
"Sebastien Gros"
] | NeurIPS.cc/2024/Conference | 2410.06474 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7t6aq0Fa9D | @inproceedings{
wu2024fastopic,
title={{FAST}opic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model},
author={Xiaobao Wu and Thong Thanh Nguyen and Delvin Ce Zhang and William Yang Wang and Anh Tuan Luu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7t6aq0Fa9D}
} | Topic models have been evolving rapidly over the years, from conventional to recent neural models. However, existing topic models generally struggle with either effectiveness, efficiency, or stability, highly impeding their practical applications. In this paper, we propose FASTopic, a fast, adaptive, stable, and transferable topic model. FASTopic follows a new paradigm: Dual Semantic-relation Reconstruction (DSR). Instead of previous conventional, VAE-based, or clustering-based methods, DSR directly models the semantic relations among document embeddings from a pretrained Transformer and learnable topic and word embeddings. By reconstructing through these semantic relations, DSR discovers latent topics. This brings about a neat and efficient topic modeling framework. We further propose a novel Embedding Transport Plan (ETP) method. Rather than early straightforward approaches, ETP explicitly regularizes the semantic relations as optimal transport plans. This addresses the relation bias issue and thus leads to effective topic modeling. Extensive experiments on benchmark datasets demonstrate that our FASTopic shows superior effectiveness, efficiency, adaptivity, stability, and transferability, compared to state-of-the-art baselines across various scenarios. | FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model | [
"Xiaobao Wu",
"Thong Thanh Nguyen",
"Delvin Ce Zhang",
"William Yang Wang",
"Anh Tuan Luu"
] | NeurIPS.cc/2024/Conference | 2405.17978 | [
"https://github.com/bobxwu/topmost"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7su2GfqvmN | @inproceedings{
lee2024contactfield,
title={ContactField: Implicit Field Representation for Multi-Person Interaction Geometry},
author={Hansol Lee and Tackgeun You and Hansoo Park and Woohyeon Shim and Sanghyeon Kim and Hwasup Lim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7su2GfqvmN}
} | We introduce a novel implicit field representation tailored for multi-person interaction geometry in 3D spaces, capable of simultaneously reconstructing occupancy, instance identification (ID) tags, and contact fields. Volumetric representation of interacting human bodies presents significant challenges, including inaccurately captured geometries, varying degrees of occlusion, and data scarcity. Existing multi-view methods, which either reconstruct each subject in isolation or merge nearby 3D surfaces into a single unified mesh, often fail to capture the intricate geometry between interacting bodies and exploit on datasets with many views and a small group of people for training. Our approach utilizes an implicit representation for interaction geometry contextualized by a multi-view local-global feature module. This module adeptly aggregates both local and global information from individual views and interacting groups, enabling precise modeling of close physical interactions through dense point retrieval in small areas, supported by the implicit fields. Furthermore, we develop a synthetic dataset encompassing diverse multi-person interaction scenarios to enhance the robustness of our geometry estimation. The experimental results demonstrate the superiority of our method to accurately reconstruct human geometries and ID tags within three-dimensional spaces, outperforming conventional multi-view techniques. Notably, our method facilitates unsupervised estimation of contact points without the need for specific training data on contact supervision. | ContactField: Implicit Field Representation for Multi-Person Interaction Geometry | [
"Hansol Lee",
"Tackgeun You",
"Hansoo Park",
"Woohyeon Shim",
"Sanghyeon Kim",
"Hwasup Lim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=7sdkLVuYCU | @inproceedings{
tseng2024qtip,
title={{QTIP}: Quantization with Trellises and Incoherence Processing},
author={Albert Tseng and Qingyao Sun and David Hou and Christopher De Sa},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7sdkLVuYCU}
} | Post-training quantization (PTQ) reduces the memory footprint of LLMs by quantizing weights to low-precision datatypes.
Since LLM inference is usually memory-bound, PTQ methods can improve inference throughput.
Recent state-of-the-art PTQ approaches use vector quantization (VQ) to quantize multiple weights at once, which improves information utilization through better shaping.
However, VQ requires a codebook with size exponential in the dimension.
This limits current VQ-based PTQ works to low VQ dimensions ($\le 8$) that in turn limit quantization quality.
Here, we introduce QTIP, which instead uses trellis coded quantization (TCQ) to achieve ultra-high-dimensional quantization.
TCQ uses a stateful decoder that separates the codebook size from the bitrate and effective dimension.
QTIP introduces a spectrum of lookup-only to computed lookup-free trellis codes designed for a hardware-efficient "bitshift" trellis structure; these codes achieve state-of-the-art results in both quantization quality and inference speed. | QTIP: Quantization with Trellises and Incoherence Processing | [
"Albert Tseng",
"Qingyao Sun",
"David Hou",
"Christopher De Sa"
] | NeurIPS.cc/2024/Conference | 2406.11235 | [
"https://github.com/Cornell-RelaxML/qtip"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=7sACcaOmGi | @inproceedings{
mhammedi2024the,
title={The Power of Resets in Online Reinforcement Learning},
author={Zakaria Mhammedi and Dylan J Foster and Alexander Rakhlin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7sACcaOmGi}
} | Simulators are a pervasive tool in reinforcement learning, but most existing algorithms cannot efficiently exploit simulator access -- particularly in high-dimensional domains that require general function approximation. We explore the power of simulators through online reinforcement learning with local simulator access (or, local planning), an RL protocol where the agent is allowed to reset to previously observed states and follow their dynamics during training. We use local simulator access to unlock new statistical guarantees that were previously out of reach:
- We show that MDPs with low coverability (Xie et al. 2023) -- a general structural condition that subsumes Block MDPs and Low-Rank MDPs -- can be learned in a sample-efficient fashion with only Q⋆-realizability (realizability of the optimal state-value function); existing online RL algorithms require significantly stronger representation conditions.
- As a consequence, we show that the notorious Exogenous Block MDP problem (Efroni et al. 2022) is tractable under local simulator access.
The results above are achieved through a computationally inefficient algorithm. We complement them with a more computationally efficient algorithm, RVFS (Recursive Value Function Search), which achieves provable sample complexity guarantees under a strengthened statistical assumption known as pushforward coverability. RVFS can be viewed as a principled, provable counterpart to a successful empirical paradigm that combines recursive search (e.g., MCTS) with value function approximation. | The Power of Resets in Online Reinforcement Learning | [
"Zakaria Mhammedi",
"Dylan J Foster",
"Alexander Rakhlin"
] | NeurIPS.cc/2024/Conference | 2404.15417 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=7s53dAJlwz | @inproceedings{
cui2024lamd,
title={{LAM}3D: Large Image-Point Clouds Alignment Model for 3D Reconstruction from Single Image},
author={Ruikai Cui and Xibin Song and Weixuan Sun and Senbo Wang and Weizhe Liu and Shenzhou Chen and Taizhang Shang and YANG LI and Nick Barnes and Hongdong Li and Pan Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7s53dAJlwz}
} | Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images. Despite their success, these models often produce 3D meshes with geometric inaccuracies, stemming from the inherent challenges of deducing 3D shapes solely from image data. In this work, we introduce a novel framework, the Large Image and Point Cloud Alignment Model (LAM3D), which utilizes 3D point cloud data to enhance the fidelity of generated 3D meshes. Our methodology begins with the development of a point-cloud-based network that effectively generates precise and meaningful latent tri-planes, laying the groundwork for accurate 3D mesh reconstruction. Building upon this, our Image-Point-Cloud Feature Alignment technique processes a single input image, aligning to the latent tri-planes to imbue image features with robust 3D information. This process not only enriches the image features but also facilitates the production of high-fidelity 3D meshes without the need for multi-view input, significantly reducing geometric distortions. Our approach achieves state-of-the-art high-fidelity 3D mesh reconstruction from a single image in just 6 seconds, and experiments on various datasets demonstrate its effectiveness. | LAM3D: Large Image-Point Clouds Alignment Model for 3D Reconstruction from Single Image | [
"Ruikai Cui",
"Xibin Song",
"Weixuan Sun",
"Senbo Wang",
"Weizhe Liu",
"Shenzhou Chen",
"Taizhang Shang",
"YANG LI",
"Nick Barnes",
"Hongdong Li",
"Pan Ji"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=7rrJQ9iWoX | @inproceedings{
he2024alphatablets,
title={AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos},
author={Yuze He and Wang Zhao and Shaohui Liu and Yubin Hu and Yushi Bai and Yu-Hui Wen and Yong-jin Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7rrJQ9iWoX}
} | We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation. By representing 3D planes as rectangles with alpha channels, AlphaTablets combine the advantages of current 2D and 3D plane representations, enabling accurate, consistent and flexible modeling of 3D planes. We derive differentiable rasterization on top of AlphaTablets to efficiently render 3D planes into images, and propose a novel bottom-up pipeline for 3D planar reconstruction from monocular videos. Starting with 2D superpixels and geometric cues from pre-trained models, we initialize 3D planes as AlphaTablets and optimize them via differentiable rendering. An effective merging scheme is introduced to facilitate the growth and refinement of AlphaTablets. Through iterative optimization and merging, we reconstruct complete and accurate 3D planes with solid surfaces and clear boundaries. Extensive experiments on the ScanNet dataset demonstrate state-of-the-art performance in 3D planar reconstruction, underscoring the great potential of AlphaTablets as a generic 3D plane representation for various applications. | AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos | [
"Yuze He",
"Wang Zhao",
"Shaohui Liu",
"Yubin Hu",
"Yushi Bai",
"Yu-Hui Wen",
"Yong-jin Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=7rWTS2wuYX | @inproceedings{
allouah2024revisiting,
title={Revisiting Ensembling in One-Shot Federated Learning},
author={Youssef Allouah and Akash Dhasade and Rachid Guerraoui and Nirupam Gupta and Anne-Marie Kermarrec and Rafael Pinot and Rafael Pires and Rishi Sharma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7rWTS2wuYX}
} | Federated Learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-Shot FL (OFL) trades the iterative exchange of models between clients and the server with a single round of communication, thereby saving substantially on communication costs. Not surprisingly, OFL exhibits a performance gap in terms of accuracy with respect to FL, especially under high data heterogeneity. We introduce Fens, a novel federated ensembling scheme that approaches the accuracy of FL with the communication efficiency of OFL. Learning in Fens proceeds in two phases: first, clients train models locally and send them to the server, similar to OFL; second, clients collaboratively train a lightweight prediction aggregator model using FL. We showcase the effectiveness of Fens through exhaustive experiments spanning several datasets and heterogeneity levels. In the particular case of heterogeneously distributed CIFAR-10 dataset, Fens achieves up to a $26.9$% higher accuracy over SOTA OFL, being only $3.1$% lower than FL. At the same time, Fens incurs at most $4.3\times$ more communication than OFL, whereas FL is at least $10.9\times$ more communication-intensive than Fens. | Revisiting Ensembling in One-Shot Federated Learning | [
"Youssef Allouah",
"Akash Dhasade",
"Rachid Guerraoui",
"Nirupam Gupta",
"Anne-Marie Kermarrec",
"Rafael Pinot",
"Rafael Pires",
"Rishi Sharma"
] | NeurIPS.cc/2024/Conference | 2411.07182 | [
"https://github.com/sacs-epfl/fens"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7qT72IGkr4 | @inproceedings{
cai2024performative,
title={Performative Control for Linear Dynamical Systems},
author={Songfu Cai and Fei Han and Xuanyu Cao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7qT72IGkr4}
} | We introduce the framework of performative control, where the policy chosen by the controller affects the underlying dynamics of the control system. This results in a sequence of policy-dependent system state data with policy-dependent temporal correlations. Following the recent literature on performative prediction \cite{perdomo2020performative}, we introduce the concept of a performatively stable control (PSC) solution. We first propose a sufficient condition for the performative control problem to admit a unique PSC solution with a problem-specific structure of distributional sensitivity propagation and aggregation. We further analyze the impacts of system stability on the existence of the PSC solution. Specifically, for almost surely stable policy-dependent dynamics, the PSC solution exists if the sum of the distributional sensitivities is small enough. However, for almost surely unstable policy-dependent dynamics, the existence of the PSC solution will necessitate a temporally backward decaying of the distributional sensitivities. We finally provide a repeated stochastic gradient descent scheme that converges to the PSC solution and analyze its non-asymptotic convergence rate. Numerical results validate our theoretical analysis. | Performative Control for Linear Dynamical Systems | [
"Songfu Cai",
"Fei Han",
"Xuanyu Cao"
] | NeurIPS.cc/2024/Conference | 2410.23251 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7qJFkuZdYo | @inproceedings{
cao2024personalized,
title={Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization},
author={Yuanpu Cao and Tianrong Zhang and Bochuan Cao and Ziyi Yin and Lu Lin and Fenglong Ma and Jinghui Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7qJFkuZdYo}
} | Researchers have been studying approaches to steer the behavior of Large Language Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning seems to be a direct solution, it requires substantial computational resources and may significantly affect the utility of the original LLM.
Recent endeavors have introduced more lightweight strategies, focusing on extracting ``steering vectors'' to guide the model's output toward desired behaviors by adjusting activations within specific layers of the LLM's transformer architecture. However, such steering vectors are directly extracted from the activations of human preference data and thus often lead to suboptimal results and occasional failures, especially in alignment-related scenarios.
In this work, we propose an innovative approach that could produce more effective steering vectors through bi-directional preference optimization.
Our method is designed to allow steering vectors to directly influence the generation probability of contrastive human preference data pairs, thereby offering a more precise representation of the target behavior. By carefully adjusting the direction and magnitude of the steering vector, we enabled personalized control over the desired behavior across a spectrum of intensities.
Extensive experimentation across various open-ended generation tasks, particularly focusing on steering AI personas, has validated the efficacy of our approach.
Moreover, we comprehensively investigate critical alignment-concerning scenarios, such as managing truthfulness, mitigating hallucination, and addressing jailbreaking attacks alongside their respective defenses. Remarkably, our method can still demonstrate outstanding steering effectiveness across these scenarios. Furthermore, we showcase the transferability of our steering vectors across different models/LoRAs and highlight the synergistic benefits of applying multiple vectors simultaneously. These findings significantly broaden the practicality and versatility of our proposed method. | Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization | [
"Yuanpu Cao",
"Tianrong Zhang",
"Bochuan Cao",
"Ziyi Yin",
"Lu Lin",
"Fenglong Ma",
"Jinghui Chen"
] | NeurIPS.cc/2024/Conference | 2406.00045 | [
"https://github.com/CaoYuanpu/BiPO"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=7qBkADV4zD | @inproceedings{
wang2024deltadeq,
title={Delta{DEQ}: Exploiting Heterogeneous Convergence for Accelerating Deep Equilibrium Iterations},
author={Zuowen Wang and Longbiao Cheng and Pehuen Moure and Niklas Hahn and Shih-Chii Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7qBkADV4zD}
} | Implicit neural networks including deep equilibrium models have achieved superior task performance with better parameter efficiency in various applications. However, it is often at the expense of higher computation costs during inference. In this work, we identify a phenomenon named $\textbf{heterogeneous convergence}$ that exists in deep equilibrium models and other iterative methods. We observe much faster convergence of state activations in certain dimensions therefore indicating the dimensionality of the underlying dynamics of the forward pass is much lower than the defined dimension of the states. We thereby propose to exploit heterogeneous convergence by storing past linear operation results (e.g., fully connected and convolutional layers) and only propagating the state activation when its change exceeds a threshold. Thus, for the already converged dimensions, the computations can be skipped. We verified our findings and reached 84\% FLOPs reduction on the implicit neural representation task, 73\% on the Sintel and 76\% on the KITTI datasets for the optical flow estimation task while keeping comparable task accuracy with the models that perform the full update. | DeltaDEQ: Exploiting Heterogeneous Convergence for Accelerating Deep Equilibrium Iterations | [
"Zuowen Wang",
"Longbiao Cheng",
"Pehuen Moure",
"Niklas Hahn",
"Shih-Chii Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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