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null | https://openreview.net/forum?id=mtyy3Myyhz | @inproceedings{
lin2024shpruner,
title={S2{HP}runer: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning},
author={Weihao Lin and Shengji Tang and Chong Yu and Peng Ye and Tao Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mtyy3Myyhz}
} | Recently, differentiable mask pruning methods optimize the continuous relaxation architecture (soft network) as the proxy of the pruned discrete network (hard network) for superior sub-architecture search. However, due to the agnostic impact of the discretization process, the hard network struggles with the equivalent representational capacity as the soft network, namely discretization gap, which severely spoils the pruning performance. In this paper, we first investigate the discretization gap and propose a novel structural differentiable mask pruning framework named S2HPruner to bridge the discretization gap in a one-stage manner. In the training procedure, SH2Pruner forwards both the soft network and its corresponding hard network, then distills the hard network under the supervision of the soft network. To optimize the mask and prevent performance degradation, we propose a decoupled bidirectional knowledge distillation. It blocks the weight updating from the hard to the soft network while maintaining the gradient corresponding to the mask. Compared with existing pruning arts, S2HPruner achieves surpassing pruning performance without fine-tuning on comprehensive benchmarks, including CIFAR-100, Tiny ImageNet, and ImageNet with a variety of network architectures. Besides, investigation and analysis experiments explain the effectiveness of S2HPruner. Codes will be released soon. | S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning | [
"Weihao Lin",
"Shengji Tang",
"Chong Yu",
"Peng Ye",
"Tao Chen"
] | NeurIPS.cc/2024/Conference | 2410.07046 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mtOPyMkSRk | @inproceedings{
pelhan2024a,
title={A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation},
author={Jer Pelhan and Alan Lukezic and Vitjan Zavrtanik and Matej Kristan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mtOPyMkSRk}
} | Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed by unsupervised image-wide object appearance aggregation.
Due to potentially diverse object appearances, the existing approaches often lead to overgeneralization and false positive detections.
Furthermore, the best-performing methods train object localization by a surrogate loss, that predicts a unit Gaussian at each object center. This loss is sensitive to annotation error, hyperparameters and does not directly optimize the detection task, leading to suboptimal counts.
We introduce GeCo, a novel low-shot counter that achieves accurate object detection, segmentation, and count estimation in a unified architecture.
GeCo robustly generalizes the prototypes across objects appearances through a novel dense object query formulation.
In addition, a novel counting loss is proposed, that directly optimizes the detection task and avoids the issues of the standard surrogate loss.
GeCo surpasses the leading few-shot detection-based counters by $\sim$25\% in the total count MAE, achieves superior detection accuracy and sets a new solid state-of-the-art result across all low-shot counting setups.
The code will be available on GitHub. | A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation | [
"Jer Pelhan",
"Alan Lukezic",
"Vitjan Zavrtanik",
"Matej Kristan"
] | NeurIPS.cc/2024/Conference | 2409.18686 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mtBmKqyqGS | @inproceedings{
wang2024phased,
title={Phased Consistency Models},
author={Fu-Yun Wang and Zhaoyang Huang and Alexander William Bergman and Dazhong Shen and Peng Gao and Michael Lingelbach and Keqiang Sun and Weikang Bian and Guanglu Song and Yu Liu and Xiaogang Wang and Hongsheng Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mtBmKqyqGS}
} | Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image generation in the latent space remains unsatisfactory. In this paper, we identify three key flaws in the current design of Latent Consistency Models~(LCMs). We investigate the reasons behind these limitations and propose Phased Consistency Models (PCMs), which generalize the design space and address the identified limitations. Our evaluations demonstrate that PCMs outperform LCMs across 1--16 step generation settings. While PCMs are specifically designed for multi-step refinement, they achieve comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show the methodology of PCMs is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. Our code is available at https://github.com/G-U-N/Phased-Consistency-Model. | Phased Consistency Models | [
"Fu-Yun Wang",
"Zhaoyang Huang",
"Alexander William Bergman",
"Dazhong Shen",
"Peng Gao",
"Michael Lingelbach",
"Keqiang Sun",
"Weikang Bian",
"Guanglu Song",
"Yu Liu",
"Xiaogang Wang",
"Hongsheng Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mpDbWjLzfT | @inproceedings{
ahmed2024contrast,
title={{CONTRAST}: Continual Multi-source Adaptation to Dynamic Distributions},
author={Sk Miraj Ahmed and Fahim Faisal Niloy and Xiangyu Chang and Dripta S. Raychaudhuri and Samet Oymak and Amit Roy-Chowdhury},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mpDbWjLzfT}
} | Adapting to dynamic data distributions is a practical yet challenging task. One effective strategy is to use a model ensemble, which leverages the diverse expertise of different models to transfer knowledge to evolving data distributions. However, this approach faces difficulties when the dynamic test distribution is available only in small batches and without access to the original source data. To address the challenge of adapting to dynamic distributions in such practical settings, we propose continual multi-source adaptation to dynamic distributions (CONTRAST), a novel method that optimally combines multiple source models to adapt to the dynamic test data. CONTRAST has two distinguishing features. First, it efficiently computes the optimal combination weights to combine the source models to adapt to the test data distribution continuously as a function of time. Second, it identifies which of the source model parameters to update so that only the model which is most correlated to the target data is adapted, leaving the less correlated ones untouched; this mitigates the issue of ``forgetting" the source model parameters by focusing only on the source model that exhibits the strongest correlation with the test batch distribution. Through theoretical analysis we show that the proposed method is able to optimally combine the source models and prioritize updates to the model least prone to forgetting. Experimental analysis on diverse datasets demonstrates that the combination of multiple source models does at least as well as the best source (with hindsight knowledge), and performance does not degrade as the test data distribution changes over time (robust to forgetting). | CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions | [
"Sk Miraj Ahmed",
"Fahim Faisal Niloy",
"Xiangyu Chang",
"Dripta S. Raychaudhuri",
"Samet Oymak",
"Amit Roy-Chowdhury"
] | NeurIPS.cc/2024/Conference | 2401.02561 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mp8u2Pcmqz | @inproceedings{
lin2024duquant,
title={DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized {LLM}s},
author={Haokun Lin and Haobo Xu and Yichen Wu and Jingzhi Cui and Yingtao Zhang and Linzhan Mou and Linqi Song and Zhenan Sun and Ying Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mp8u2Pcmqz}
} | Quantization of large language models (LLMs) faces significant challenges, particularly due to the presence of outlier activations that impede efficient low-bit representation. Traditional approaches predominantly address Normal Outliers, which are activations across all tokens with relatively large magnitudes. However, these methods struggle with smoothing Massive Outliers that display significantly larger values, which leads to significant performance degradation in low-bit quantization. In this paper, we introduce DuQuant, a novel approach that utilizes rotation and permutation transformations to more effectively mitigate both massive and normal outliers. First, DuQuant starts by constructing the rotation matrix, using specific outlier dimensions as prior knowledge, to redistribute outliers to adjacent channels by block-wise rotation. Second, We further employ a zigzag permutation to balance the distribution of outliers across blocks, thereby reducing block-wise variance. A subsequent rotation further smooths the activation landscape, enhancing model performance. DuQuant simplifies the quantization process and excels in managing outliers, outperforming the state-of-the-art baselines across various sizes and types of LLMs on multiple tasks, even with 4-bit weight-activation quantization. Our code is available at https://github.com/Hsu1023/DuQuant. | DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs | [
"Haokun Lin",
"Haobo Xu",
"Yichen Wu",
"Jingzhi Cui",
"Yingtao Zhang",
"Linzhan Mou",
"Linqi Song",
"Zhenan Sun",
"Ying Wei"
] | NeurIPS.cc/2024/Conference | 2406.01721 | [
"https://github.com/hsu1023/duquant"
] | https://huggingface.co/papers/2406.01721 | 1 | 0 | 0 | 9 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=mp6OWpDIJC | @inproceedings{
liu2024autonomous,
title={Autonomous Agents for Collaborative Task under Information Asymmetry},
author={Wei Liu and Chenxi Wang and YiFei Wang and Zihao Xie and Rennai Qiu and Yufan Dang and Zhuoyun Du and Weize Chen and Cheng Yang and Chen Qian},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mp6OWpDIJC}
} | Large Language Model Multi-Agent Systems (LLM-MAS) have greatly progressed in solving complex tasks. It communicates among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' collaborations are leveraged to perform multi-person tasks, a new challenge arises due to information asymmetry, since each agent can only access the information of its human user. Previous MAS struggle to complete tasks under this condition. To address this, we propose a new MAS paradigm termed iAgents, which denotes Informative Multi-Agent Systems. In iAgents, the human social network is mirrored in the agent network, where agents proactively exchange human information necessary for task resolution, thereby overcoming information asymmetry. iAgents employs a novel agent reasoning mechanism, InfoNav, to navigate agents' communication towards effective information exchange. Together with InfoNav, iAgents organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange. Additionally, we introduce InformativeBench, the first benchmark tailored for evaluating LLM agents' task-solving ability under information asymmetry. Experimental results show that iAgents can collaborate within a social network of 140 individuals and 588 relationships, autonomously communicate over 30 turns, and retrieve information from nearly 70,000 messages to complete tasks within 3 minutes. | Autonomous Agents for Collaborative Task under Information Asymmetry | [
"Wei Liu",
"Chenxi Wang",
"YiFei Wang",
"Zihao Xie",
"Rennai Qiu",
"Yufan Dang",
"Zhuoyun Du",
"Weize Chen",
"Cheng Yang",
"Chen Qian"
] | NeurIPS.cc/2024/Conference | 2406.14928 | [
"https://github.com/thinkwee/iAgents"
] | https://huggingface.co/papers/2406.14928 | 2 | 1 | 0 | 10 | [] | [] | [
"thinkwee/iAgents"
] | [] | [] | [
"thinkwee/iAgents"
] | 1 | poster |
null | https://openreview.net/forum?id=motImXq3B1 | @inproceedings{
wang2024pcnet,
title={P\${\textasciicircum}2\$C\${\textasciicircum}2\$Net: {PDE}-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics},
author={Qi Wang and Pu Ren and Hao Zhou and Xin-Yang Liu and Zhiwen Deng and Yi Zhang and Ruizhi Chengze and Hongsheng Liu and Zidong Wang and Jian-Xun Wang and Ji-Rong Wen and Hao Sun and Yang Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=motImXq3B1}
} | When solving partial differential equations (PDEs), classical numerical methods often require fine mesh grids and small time stepping to meet stability, consistency, and convergence conditions, leading to high computational cost. Recently, machine learning has been increasingly utilized to solve PDE problems, but they often encounter challenges related to interpretability, generalizability, and strong dependency on rich labeled data. Hence, we introduce a new PDE-Preserved Coarse Correction Network (P$^2$C$^2$Net) to efficiently solve spatiotemporal PDE problems on coarse mesh grids in small data regimes. The model consists of two synergistic modules: (1) a trainable PDE block that learns to update the coarse solution (i.e., the system state), based on a high-order numerical scheme with boundary condition encoding, and (2) a neural network block that consistently corrects the solution on the fly. In particular, we propose a learnable symmetric Conv filter, with weights shared over the entire model, to accurately estimate the spatial derivatives of PDE based on the neural-corrected system state. The resulting physics-encoded model is capable of handling limited training data (e.g., 3--5 trajectories) and accelerates the prediction of PDE solutions on coarse spatiotemporal grids while maintaining a high accuracy. P$^2$C$^2$Net achieves consistent state-of-the-art performance with over 50\% gain (e.g., in terms of relative prediction error) across four datasets covering complex reaction-diffusion processes and turbulent flows. | P^2C^2Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics | [
"Qi Wang",
"Pu Ren",
"Hao Zhou",
"Xin-Yang Liu",
"Zhiwen Deng",
"Yi Zhang",
"Ruizhi Chengze",
"Hongsheng Liu",
"Zidong Wang",
"Jian-Xun Wang",
"Ji-Rong Wen",
"Hao Sun",
"Yang Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mmSFfib6pI | @inproceedings{
garrett2024validating,
title={Validating Climate Models with Spherical Convolutional Wasserstein Distance},
author={Robert C. Garrett and Trevor Harris and Zhuo Wang and Bo Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mmSFfib6pI}
} | The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data. This new similarity measure accounts for spatial variability using convolutional projections and quantifies local differences in the distribution of climate variables. We apply this method to evaluate the historical model outputs of the Coupled Model Intercomparison Project (CMIP) members by comparing them to observational and reanalysis data products. Additionally, we investigate the progression from CMIP phase 5 to phase 6 and find modest improvements in the phase 6 models regarding their ability to produce realistic climatologies. | Validating Climate Models with Spherical Convolutional Wasserstein Distance | [
"Robert C. Garrett",
"Trevor Harris",
"Zhuo Wang",
"Bo Li"
] | NeurIPS.cc/2024/Conference | 2401.14657 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=mlmTxJwVsb | @inproceedings{
tu2024dmnet,
title={{DMN}et: Self-comparison Driven Model for Subject-independent Seizure Detection},
author={Shihao Tu and Linfeng Cao and Daoze Zhang and Junru Chen and Lvbin Ma and Yin Zhang and Yang Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mlmTxJwVsb}
} | Automated seizure detection (ASD) using intracranial electroencephalography (iEEG) is critical for effective epilepsy treatment. However, the significant domain shift of iEEG signals across subjects poses a major challenge, limiting their applicability in real-world clinical scenarios. In this paper, we address this issue by analyzing the primary cause behind the failure of existing iEEG models for subject-independent seizure detection, and identify a critical universal seizure pattern: seizure events consistently exhibit higher average amplitude compared to adjacent normal events. To mitigate the domain shifts and preserve the universal seizure patterns, we propose a novel self-comparison mechanism. This mechanism effectively aligns iEEG signals across subjects and time intervals. Building upon these findings, we propose Difference Matrix-based Neural Network (DMNet), a subject-independent seizure detection model, which leverages self-comparison based on two constructed (contextual, channel-level) references to mitigate shifts of iEEG, and utilize a simple yet effective difference matrix to encode the universal seizure patterns. Extensive experiments show that DMNet significantly outperforms previous SOTAs while maintaining high efficiency on a real-world clinical dataset collected by us and two public datasets for subject-independent seizure detection. Moreover, the visualization results demonstrate that the generated difference matrix can effectively capture the seizure activity changes during the seizure evolution process. Additionally, we deploy our method in an online diagnosis system to illustrate its effectiveness in real clinical applications. | DMNet: Self-comparison Driven Model for Subject-independent Seizure Detection | [
"Shihao Tu",
"Linfeng Cao",
"Daoze Zhang",
"Junru Chen",
"Lvbin Ma",
"Yin Zhang",
"Yang Yang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mlm3nUwOeQ | @inproceedings{
sun2024tight,
title={Tight Rates for Bandit Control Beyond Quadratics},
author={Y. Jennifer Sun and Zhou Lu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mlm3nUwOeQ}
} | Unlike classical control theory, such as Linear Quadratic Control (LQC), real-world control problems are highly complex. These problems often involve adversarial perturbations, bandit feedback models, and non-quadratic, adversarially chosen cost functions. A fundamental yet unresolved question is whether optimal regret can be achieved for these general control problems. The standard approach to addressing this problem involves a reduction to bandit convex optimization with memory. In the bandit setting, constructing a gradient estimator with low variance is challenging due to the memory structure and non-quadratic loss functions.
In this paper, we provide an affirmative answer to this question. Our main contribution is an algorithm that achieves an $\tilde{O}(\sqrt{T})$ optimal regret for bandit non-stochastic control with strongly-convex and smooth cost functions in the presence of adversarial perturbations, improving the previously known $\tilde{O}(T^{2/3})$ regret bound from \citep{cassel2020bandit}. Our algorithm overcomes the memory issue by reducing the problem to Bandit Convex Optimization (BCO) without memory and addresses general strongly-convex costs using recent advancements in BCO from \citep{suggala2024second}. Along the way, we develop an improved algorithm for BCO with memory, which may be of independent interest. | Tight Rates for Bandit Control Beyond Quadratics | [
"Y. Jennifer Sun",
"Zhou Lu"
] | NeurIPS.cc/2024/Conference | 2410.00993 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mljDUaQpln | @inproceedings{
morishita2024enhancing,
title={Enhancing Reasoning Capabilities of {LLM}s via Principled Synthetic Logic Corpus},
author={Terufumi Morishita and Gaku Morio and Atsuki Yamaguchi and Yasuhiro Sogawa},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mljDUaQpln}
} | Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning.
To address this, we propose $\textbf{Additional Logic Training (ALT)}$, which aims to enhance LLMs' reasoning capabilities by program-generated logical reasoning samples.
We first establish principles for designing high-quality samples by integrating symbolic logic theory and previous empirical insights.
Then, based on these principles, we construct a synthetic corpus named $\textbf{Formal} \ \textbf{Logic} \ \textbf{\textit{D}eduction} \ \textbf{\textit{D}iverse}$ (FLD$^{\times2}$), comprising numerous samples of multi-step deduction with unknown facts, diverse reasoning rules, diverse linguistic expressions, and challenging distractors.
Finally, we empirically show that ALT on FLD$^{\times2}$ substantially enhances the reasoning capabilities of state-of-the-art LLMs, including LLaMA-3.1-70B.
Improvements include gains of up to 30 points on logical reasoning benchmarks, up to 10 points on math and coding benchmarks, and 5 points on the benchmark suite BBH. | Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus | [
"Terufumi Morishita",
"Gaku Morio",
"Atsuki Yamaguchi",
"Yasuhiro Sogawa"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ml01XyP698 | @inproceedings{
wang2024freesplat,
title={FreeSplat: Generalizable 3D Gaussian Splatting Towards Free View Synthesis of Indoor Scenes},
author={Yunsong Wang and Tianxin Huang and Hanlin Chen and Gim Hee Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ml01XyP698}
} | Empowering 3D Gaussian Splatting with generalization ability is appealing. However, existing generalizable 3D Gaussian Splatting methods are largely confined to narrow-range interpolation between stereo images due to their heavy backbones, thus lacking the ability to accurately localize 3D Gaussian and support free-view synthesis across wide view range. In this paper, we present a novel framework FreeSplat that is capable of reconstructing geometrically consistent 3D scenes from long sequence input towards free-view synthesis.Specifically, we firstly introduce Low-cost Cross-View Aggregation achieved by constructing adaptive cost volumes among nearby views and aggregating features using a multi-scale structure. Subsequently, we present the Pixel-wise Triplet Fusion to eliminate redundancy of 3D Gaussians in overlapping view regions and to aggregate features observed across multiple views. Additionally, we propose a simple but effective free-view training strategy that ensures robust view synthesis across broader view range regardless of the number of views. Our empirical results demonstrate state-of-the-art novel view synthesis peformances in both novel view rendered color maps quality and depth maps accuracy across different numbers of input views. We also show that FreeSplat performs inference more efficiently and can effectively reduce redundant Gaussians, offering the possibility of feed-forward large scene reconstruction without depth priors. Our code will be made open-source upon paper acceptance. | FreeSplat: Generalizable 3D Gaussian Splatting Towards Free View Synthesis of Indoor Scenes | [
"Yunsong Wang",
"Tianxin Huang",
"Hanlin Chen",
"Gim Hee Lee"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/wangys16/freesplat"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mkzpN2T87C | @inproceedings{
jin2024nonasymptotic,
title={Non-asymptotic Global Convergence Analysis of {BFGS} with the Armijo-Wolfe Line Search},
author={Qiujiang Jin and Ruichen Jiang and Aryan Mokhtari},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mkzpN2T87C}
} | In this paper, we present the first explicit and non-asymptotic global convergence rates of the BFGS method when implemented with an inexact line search scheme satisfying the Armijo-Wolfe conditions. We show that BFGS achieves a global linear convergence rate of $(1 - \frac{1}{\kappa})^t$ for $\mu$-strongly convex functions with $L$-Lipschitz gradients, where $\kappa = \frac{L}{\mu}$ represents the condition number. Additionally, if the objective function's Hessian is Lipschitz, BFGS with the Armijo-Wolfe line search achieves a linear convergence rate that depends solely on the line search parameters, independent of the condition number. We also establish a global superlinear convergence rate of $\mathcal{O}((\frac{1}{t})^t)$. These global bounds are all valid for any starting point $x_0$ and any symmetric positive definite initial Hessian approximation matrix $B_0$, though the choice of $B_0$ impacts the number of iterations needed to achieve these rates. By synthesizing these results, we outline the first global complexity characterization of BFGS with the Armijo-Wolfe line search. Additionally, we clearly define a mechanism for selecting the step size to satisfy the Armijo-Wolfe conditions and characterize its overall complexity. | Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search | [
"Qiujiang Jin",
"Ruichen Jiang",
"Aryan Mokhtari"
] | NeurIPS.cc/2024/Conference | 2404.16731 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=mkw6x0OExg | @inproceedings{
puli2024explanations,
title={Explanations that reveal all through the definition of encoding},
author={Aahlad Manas Puli and Nhi Nguyen and Rajesh Ranganath},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mkw6x0OExg}
} | Feature attributions attempt to highlight what inputs drive predictive power. Good attributions or explanations are thus those that produce inputs that retain this predictive power; accordingly, evaluations of explanations score their quality of prediction. However, evaluations produce scores better than what appears possible from the values in the explanation for a class of explanations, called encoding explanations. Probing for encoding remains a challenge because there is no general characterization of what gives the extra predictive power. We develop a definition of encoding that identifies this extra predictive power via conditional dependence and show that the definition fits existing examples of encoding. This definition implies, in contrast to encoding explanations, that non-encoding explanations contain all the informative inputs used to produce the explanation, giving them a “what you see is what you get” property, which makes them transparent and simple to use. Next, we prove that existing scores (ROAR, FRESH, EVAL-X) do not rank non-encoding explanations above encoding ones, and develop STRIPE-X which ranks them correctly. After empirically demonstrating the theoretical insights, we use STRIPE-X to uncover encoding in LLM-generated explanations for predicting the sentiment in movie reviews. | Explanations that reveal all through the definition of encoding | [
"Aahlad Manas Puli",
"Nhi Nguyen",
"Rajesh Ranganath"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mjGy8g3pgi | @inproceedings{
nguyen2024yollava,
title={Yo'{LL}a{VA}: Your Personalized Language and Vision Assistant},
author={Thao Nguyen and Haotian Liu and Yuheng Li and Mu Cai and Utkarsh Ojha and Yong Jae Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mjGy8g3pgi}
} | Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering).
While broad, their knowledge remains generic (e.g., recognizing a dog), and they are unable to handle personalized subjects (e.g., recognizing a user's pet dog).
Human reasoning, in contrast, typically operates within the context of specific subjects in our surroundings. For example, one might ask, "What should I buy for *my dog*'s birthday?"; as opposed to a generic inquiry about "What should I buy for *a dog*'s birthday?".
Similarly, when looking at a friend's image, the interest lies in seeing their activities (e.g., "*my friend* is holding a cat"), rather than merely observing generic human actions (e.g., "*a man* is holding a cat").
In this paper, we introduce the novel task of personalizing LMMs, so that they can have conversations about a specific subject. We propose Yo'LLaVA, which learns to embed a personalized subject into a set of latent tokens given a handful of example images of the subject. Our qualitative and quantitative analyses reveal that Yo'LLaVA can learn the concept more efficiently using fewer tokens and more effectively encode the visual attributes compared to strong prompting baselines (e.g., LLaVA). | Yo'LLaVA: Your Personalized Language and Vision Assistant | [
"Thao Nguyen",
"Haotian Liu",
"Yuheng Li",
"Mu Cai",
"Utkarsh Ojha",
"Yong Jae Lee"
] | NeurIPS.cc/2024/Conference | 2406.09400 | [
""
] | https://huggingface.co/papers/2406.09400 | 0 | 1 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=mirkQqx6po | @inproceedings{
cohen-addad2024learningaugmented,
title={Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems},
author={Vincent Cohen-Addad and Tommaso d'Orsi and Anupam Gupta and Euiwoong Lee and Debmalya Panigrahi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mirkQqx6po}
} | In recent years, there has been a surge of interest in the use of machine-learned predictions to bypass worst-case lower bounds for classical problems in combinatorial optimization. So far, the focus has mostly been on online algorithms, where information-theoretic barriers are overcome using predictions about the unknown future. In this paper, we consider the complementary question of using learned information to overcome computational barriers in the form of approximation hardness of polynomial-time algorithms for NP-hard (offline) problems. We show that noisy predictions about the optimal solution can be used to break classical hardness results for maximization problems such as the max-cut problem and more generally, maximization versions of constraint satisfaction problems (CSPs). | Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems | [
"Vincent Cohen-Addad",
"Tommaso d'Orsi",
"Anupam Gupta",
"Euiwoong Lee",
"Debmalya Panigrahi"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=miO8odRzto | @inproceedings{
kang2024online,
title={Online Relational Inference for Evolving Multi-agent Interacting Systems},
author={Beomseok Kang and Priyabrata Saha and Sudarshan Sharma and Biswadeep Chakraborty and Saibal Mukhopadhyay},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=miO8odRzto}
} | We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point, thereby allowing it to adapt to changing environments in real-time. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning rate technique called AdaRelation, which adjusts based on the historical sensitivity of the decoder to changes in the interaction graph. Additionally, a data augmentation method named Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on both synthetic datasets and real-world data (CMU MoCap for human motion) demonstrate that ORI significantly improves the accuracy and adaptability of relational inference in dynamic settings compared to existing methods. This approach is model-agnostic, enabling seamless integration with various neural relational inference (NRI) architectures, and offers a robust solution for real-time applications in complex, evolving systems. | Online Relational Inference for Evolving Multi-agent Interacting Systems | [
"Beomseok Kang",
"Priyabrata Saha",
"Sudarshan Sharma",
"Biswadeep Chakraborty",
"Saibal Mukhopadhyay"
] | NeurIPS.cc/2024/Conference | 2411.01442 | [
"https://github.com/beomseokg/ORI"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mhhlZeAr67 | @inproceedings{
rodemann2024reciprocal,
title={Reciprocal Learning},
author={Julian Rodemann and Christoph Jansen and Georg Schollmeyer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mhhlZeAr67}
} | We demonstrate that numerous machine learning algorithms are specific instances of one single paradigm: reciprocal learning. These instances range from active learning over multi-armed bandits to self-training. We show that all these algorithms not only learn parameters from data but also vice versa: They iteratively alter training data in a way that depends on the current model fit. We introduce reciprocal learning as a generalization of these algorithms using the language of decision theory. This allows us to study under what conditions they converge. The key is to guarantee that reciprocal learning contracts such that the Banach fixed-point theorem applies. In this way, we find that reciprocal learning converges at linear rates to an approximately optimal model under some assumptions on the loss function, if their predictions are probabilistic and the sample adaption is both non-greedy and either randomized or regularized. We interpret these findings and provide corollaries that relate them to active learning, self-training, and bandits. | Reciprocal Learning | [
"Julian Rodemann",
"Christoph Jansen",
"Georg Schollmeyer"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mfvKEdJ4zW | @inproceedings{
fumero2024latent,
title={Latent Functional Maps: a spectral framework for representation alignment},
author={Marco Fumero and Marco Pegoraro and Valentino Maiorca and Francesco Locatello and Emanuele Rodol{\`a}},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mfvKEdJ4zW}
} | Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these representational spaces is an ongoing challenge.
By integrating spectral geometry principles into neural modeling, we show that this problem can be better addressed in the functional domain, mitigating complexity, while enhancing interpretability and performances on downstream tasks.
To this end, we introduce a multi-purpose framework to the representation learning community, which allows to: (i) compare different spaces in an interpretable way and measure their intrinsic similarity; (ii) find correspondences between them, both in unsupervised and weakly supervised settings, and (iii) to effectively transfer representations between distinct spaces.
We validate our framework on various applications, ranging from stitching to retrieval tasks, and on multiple modalities, demonstrating that Latent Functional Maps can serve as a swiss-army knife for representation alignment. | Latent Functional Maps: a spectral framework for representation alignment | [
"Marco Fumero",
"Marco Pegoraro",
"Valentino Maiorca",
"Francesco Locatello",
"Emanuele Rodolà"
] | NeurIPS.cc/2024/Conference | 2406.14183 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mfTvNzhsht | @inproceedings{
branzei2024dueling,
title={Dueling over Dessert, Mastering the Art of Repeated Cake Cutting},
author={Simina Branzei and MohammadTaghi Hajiaghayi and Reed Phillips and Suho Shin and Kun Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mfTvNzhsht}
} | We consider the setting of repeated fair division between two players, denoted Alice and Bob, with private valuations over a cake. In each round, a new cake arrives, which is identical to the ones in previous rounds. Alice cuts the cake at a point of her choice, while Bob chooses the left piece or the right piece, leaving the remainder for Alice.
We consider two versions: sequential, where Bob observes Alice's cut point before choosing left/right, and simultaneous, where he only observes her cut point after making his choice. The simultaneous version was first considered by Aumann and Maschler.
We observe that if Bob is almost myopic and chooses his favorite piece too often, then he can be systematically exploited by Alice through a strategy akin to a binary search. This strategy allows Alice to approximate Bob's preferences with increasing precision, thereby securing a disproportionate share of the resource over time.
We analyze the limits of how much a player can exploit the other one and show that fair utility profiles are in fact achievable. Specifically, the players can enforce the equitable utility profile of $(1/2, 1/2)$ in the limit on every trajectory of play, by keeping the other player's utility to approximately $1/2$ on average while guaranteeing they themselves get at least approximately $1/2$ on average. We show this theorem using a connection with Blackwell approachability.
Finally, we analyze a natural dynamic known as fictitious play, where players best respond to the empirical distribution of the other player. We show that
fictitious play converges to the equitable utility profile of $(1/2, 1/2)$ at a rate of $O(1/\sqrt{T})$. | Dueling over Dessert, Mastering the Art of Repeated Cake Cutting | [
"Simina Branzei",
"MohammadTaghi Hajiaghayi",
"Reed Phillips",
"Suho Shin",
"Kun Wang"
] | NeurIPS.cc/2024/Conference | 2402.08547 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=merJ77Jipt | @inproceedings{
ma2024diffpo,
title={Diff{PO}: A causal diffusion model for learning distributions of potential outcomes},
author={Yuchen Ma and Valentyn Melnychuk and Jonas Schweisthal and Stefan Feuerriegel},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=merJ77Jipt}
} | Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are largely limited to point estimates of potential outcomes with no uncertain quantification; thus, the full information about the distributions of potential outcomes is typically ignored. In this paper, we propose a novel causal diffusion model called DiffPO, which is carefully designed for reliable inferences in medicine by learning the distribution of potential outcomes. In our DiffPO, we leverage a tailored conditional denoising diffusion model to learn complex distributions, where we address the selection bias through a novel orthogonal diffusion loss. Another strength of our DiffPO method is that it is highly flexible (e.g., it can also be used to estimate different causal quantities such as CATE). Across a wide range of experiments, we show that our method achieves state-of-the-art performance. | DiffPO: A causal diffusion model for learning distributions of potential outcomes | [
"Yuchen Ma",
"Valentyn Melnychuk",
"Jonas Schweisthal",
"Stefan Feuerriegel"
] | NeurIPS.cc/2024/Conference | 2410.08924 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=me1MpmENpw | @inproceedings{
zion2024semantics,
title={Semantics and Spatiality of Emergent Communication},
author={Rotem Ben Zion and Boaz Carmeli and Orr Paradise and Yonatan Belinkov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=me1MpmENpw}
} | When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols. Good task performance is often considered sufficient evidence that meaningful communication is taking place, but existing empirical results show that communication strategies induced by common objectives can be counterintuitive whilst solving the task nearly perfectly. In this work, we identify a goal-agnostic prerequisite to meaningful communication, which we term semantic consistency, based on the idea that messages should have similar meanings across instances. We provide a formal definition for this idea, and use it to compare the two most common objectives in the field of emergent communication: discrimination and reconstruction. We prove, under mild assumptions, that semantically inconsistent communication protocols can be optimal solutions to the discrimination task, but not to reconstruction. We further show that the reconstruction objective encourages a stricter property, spatial meaningfulness, which also accounts for the distance between messages. Experiments with emergent communication games validate our theoretical results. These findings demonstrate an inherent advantage of distance-based communication goals, and contextualize previous empirical discoveries. | Semantics and Spatiality of Emergent Communication | [
"Rotem Ben Zion",
"Boaz Carmeli",
"Orr Paradise",
"Yonatan Belinkov"
] | NeurIPS.cc/2024/Conference | 2411.10173 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mdWz5koY5p | @inproceedings{
chen2024rgmdt,
title={{RGMDT}: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space},
author={Jingdi Chen and Hanhan Zhou and Yongsheng Mei and Carlee Joe-Wong and Gina Adam and Nathaniel D. Bastian and Tian Lan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mdWz5koY5p}
} | Deep Reinforcement Learning (DRL) algorithms have achieved great success in solving many challenging tasks while their black-box nature hinders interpretability and real-world applicability, making it difficult for human experts to interpret and understand DRL policies.
Existing works on interpretable reinforcement learning have shown promise in extracting decision tree (DT) based policies from DRL policies with most focus on the single-agent settings while prior attempts to introduce DT policies in multi-agent scenarios mainly focus on heuristic designs which do not provide any quantitative guarantees on the expected return.
In this paper, we establish an upper bound on the return gap between the oracle expert policy and an optimal decision tree policy. This enables us to recast the DT extraction problem into a novel non-euclidean clustering problem over the local observation and action values space of each agent, with action values as cluster labels and the upper bound on the return gap as clustering loss.
Both the algorithm and the upper bound are extended to multi-agent decentralized DT extractions by an iteratively-grow-DT procedure guided by an action-value function conditioned on the current DTs of other agents. Further, we propose the Return-Gap-Minimization Decision Tree (RGMDT) algorithm, which is a surprisingly simple design and is integrated with reinforcement learning through the utilization of a novel Regularized Information Maximization loss. Evaluations on tasks like D4RL show that RGMDT significantly outperforms heuristic DT-based baselines and can achieve nearly optimal returns under given DT complexity constraints (e.g., maximum number of DT nodes). | RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space | [
"Jingdi Chen",
"Hanhan Zhou",
"Yongsheng Mei",
"Carlee Joe-Wong",
"Gina Adam",
"Nathaniel D. Bastian",
"Tian Lan"
] | NeurIPS.cc/2024/Conference | 2410.16517 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mcY221BgKi | @inproceedings{
ruan2024learning,
title={Learning Cooperative Trajectory Representations for Motion Forecasting},
author={Hongzhi Ruan and Haibao Yu and Wenxian Yang and Siqi Fan and Zaiqing Nie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mcY221BgKi}
} | Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities.
Existing research mainly focuses on leveraging single-frame cooperative information to enhance the limited perception capability of the ego vehicle, while underutilizing the motion and interaction context of traffic participants observed from cooperative devices.
In this paper, we propose a forecasting-oriented representation paradigm to utilize motion and interaction features from cooperative information.
Specifically, we present V2X-Graph, a representative framework to achieve interpretable and end-to-end trajectory feature fusion for cooperative motion forecasting.
V2X-Graph is evaluated on V2X-Seq in vehicle-to-infrastructure (V2I) scenarios.
To further evaluate on vehicle-to-everything (V2X) scenario, we construct the first real-world V2X motion forecasting dataset V2X-Traj, which contains multiple autonomous vehicles and infrastructure in every scenario.
Experimental results on both V2X-Seq and V2X-Traj show the advantage of our method.
We hope both V2X-Graph and V2X-Traj will benefit the further development of cooperative motion forecasting.
Find the project at https://github.com/AIR-THU/V2X-Graph. | Learning Cooperative Trajectory Representations for Motion Forecasting | [
"Hongzhi Ruan",
"Haibao Yu",
"Wenxian Yang",
"Siqi Fan",
"Zaiqing Nie"
] | NeurIPS.cc/2024/Conference | 2311.00371 | [
"https://github.com/air-thu/v2x-graph"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=manHbkpIW6 | @inproceedings{
dong2024once,
title={Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge},
author={Fang Dong and Mengyi Chen and Jixian Zhou and Yubin Shi and Yixuan Chen and Mingzhi Dong and Yujiang Wang and Dongsheng Li and Xiaochen Yang and Rui Zhu and Robert P. Dick and Qin Lv and Fan Yang and Tun Lu and Ning Gu and Li Shang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=manHbkpIW6}
} | Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice.
However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications.
In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and long-tail distributions as the leading cause for pretrained LMs' inferior downstream performance.
Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance.
Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs.
During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers.
With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, outlier clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models. | Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge | [
"Fang Dong",
"Mengyi Chen",
"Jixian Zhou",
"Yubin Shi",
"Yixuan Chen",
"Mingzhi Dong",
"Yujiang Wang",
"Dongsheng Li",
"Xiaochen Yang",
"Rui Zhu",
"Robert P. Dick",
"Qin Lv",
"Fan Yang",
"Tun Lu",
"Ning Gu",
"Li Shang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mZwilh3hd2 | @inproceedings{
farina2024polynomialtime,
title={Polynomial-Time Computation of Exact \${\textbackslash}Phi\$-Equilibria in Polyhedral Games},
author={Gabriele Farina and Charilaos Pipis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mZwilh3hd2}
} | It is a well-known fact that correlated equilibria can be computed in polynomial time in a large class of concisely represented games using the celebrated Ellipsoid Against Hope algorithm \citep{Papadimitriou2008:Computing, Jiang2015:Polynomial}. However, the landscape of efficiently computable equilibria in sequential (extensive-form) games remains unknown. The Ellipsoid Against Hope does not apply directly to these games, because they do not have the required ``polynomial type'' property. Despite this barrier, \citet{Huang2008:Computing} altered the algorithm to compute exact extensive-form correlated equilibria.
In this paper, we generalize the Ellipsoid Against Hope and develop a simple algorithmic framework for efficiently computing saddle-points in bilinear zero-sum games, even when one of the dimensions is exponentially large. Moreover, the framework only requires a ``good-enough-response'' oracle, which is a weakened notion of a best-response oracle.
Using this machinery, we develop a general algorithmic framework for computing exact linear $\Phi$-equilibria in any polyhedral game (under mild assumptions), including correlated equilibria in normal-form games, and extensive-form correlated equilibria in extensive-form games. This enables us to give the first polynomial-time algorithm for computing exact linear-deviation correlated equilibria in extensive-form games, thus resolving an open question by \citet{Farina2023:Polynomial}. Furthermore, even for the cases for which a polynomial time algorithm for exact equilibria was already known, our framework provides a conceptually simpler solution. | Polynomial-Time Computation of Exact Φ-Equilibria in Polyhedral Games | [
"Gabriele Farina",
"Charilaos Pipis"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=mZsvm58FPG | @inproceedings{
dong2024ecmamba,
title={{ECM}amba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction},
author={Wei Dong and Han Zhou and Yulun Zhang and Xiaohong Liu and Jun Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mZsvm58FPG}
} | Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have shown promising results, few have fully embedded Retinex theory into their architecture, highlighting a gap in current methodologies. Additionally, the balance between high performance and efficiency remains an under-explored problem for exposure correction task. Inspired by Mamba which demonstrates powerful and highly efficient sequence modeling, we introduce a novel framework based on \textbf{Mamba} for \textbf{E}xposure \textbf{C}orrection (\textbf{ECMamba}) with dual pathways, each dedicated to the restoration of reflectance and illumination map, respectively. Specifically, we firstly derive the Retinex theory and we train a Retinex estimator capable of mapping inputs into two intermediary spaces, each approximating the target reflectance and illumination map, respectively. This setup facilitates the refined restoration process of the subsequent \textbf{E}xposure \textbf{C}orrection \textbf{M}amba \textbf{M}odule (\textbf{ECMM}). Moreover, we develop a novel \textbf{2D S}elective \textbf{S}tate-space layer guided by \textbf{Retinex} information (\textbf{Retinex-SS2D}) as the core operator of \textbf{ECMM}. This architecture incorporates an innovative 2D scanning strategy based on deformable feature aggregation, thereby enhancing both efficiency and effectiveness. Extensive experiment results and comprehensive ablation studies demonstrate the outstanding performance and the importance of each component of our proposed ECMamba. Code is available at \url{https://github.com/LowlevelAI/ECMamba}. | ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction | [
"Wei Dong",
"Han Zhou",
"Yulun Zhang",
"Xiaohong Liu",
"Jun Chen"
] | NeurIPS.cc/2024/Conference | 2410.21535 | [
"https://github.com/lowlevelai/ecmamba"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mZHbkbYWTp | @inproceedings{
damiani2024stochastic,
title={Stochastic Optimal Control and Estimation with Multiplicative and Internal Noise},
author={Francesco Damiani and Akiyuki Anzai and Jan Drugowitsch and Gregory C DeAngelis and Rub{\'e}n Moreno-Bote},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mZHbkbYWTp}
} | A pivotal brain computation relies on the ability to sustain perception-action loops. Stochastic optimal control theory offers a mathematical framework to explain these processes at the algorithmic level through optimality principles. However, incorporating a realistic noise model of the sensorimotor system — accounting for multiplicative noise in feedback and motor output, as well as internal noise in estimation — makes the problem challenging. Currently, the algorithm that is commonly used is the one proposed in the seminal study in (Todorov, 2005). After discovering some pitfalls in the original derivation, i.e., unbiased estimation does not hold, we improve the algorithm by proposing an efficient gradient descent-based optimization that minimizes the cost-to-go while only imposing linearity of the control law. The optimal solution is obtained by iteratively propagating in closed form the sufficient statistics to compute the expected cost and then minimizing this cost with respect to the filter and control gains. We demonstrate that this approach results in a significantly lower overall cost than current state-of-the-art solutions, particularly in the presence of internal noise, though the improvement is present in other circumstances as well, with theoretical explanations for this enhanced performance. Providing the optimal control law is key for inverse control inference, especially in explaining behavioral data under rationality assumptions. | Stochastic Optimal Control and Estimation with Multiplicative and Internal Noise | [
"Francesco Damiani",
"Akiyuki Anzai",
"Jan Drugowitsch",
"Gregory C DeAngelis",
"Rubén Moreno-Bote"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mYEjc7qGRA | @inproceedings{
zhang2024towards,
title={Towards Robust Multimodal Sentiment Analysis with Incomplete Data},
author={Haoyu Zhang and Wenbin Wang and Tianshu Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mYEjc7qGRA}
} | The field of Multimodal Sentiment Analysis (MSA) has recently witnessed an emerging direction seeking to tackle the issue of data incompleteness. Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The proposed LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model's robustness across various noise scenarios by ensuring the quality of dominant modality representations. Aside from the methodical design, we perform comprehensive experiments under random data missing scenarios, utilizing diverse and meaningful settings on several popular datasets (e.g., MOSI, MOSEI, and SIMS), providing additional uniformity, transparency, and fairness compared to existing evaluations in the literature. Empirically, LNLN consistently outperforms existing baselines, demonstrating superior performance across these challenging and extensive evaluation metrics. | Towards Robust Multimodal Sentiment Analysis with Incomplete Data | [
"Haoyu Zhang",
"Wenbin Wang",
"Tianshu Yu"
] | NeurIPS.cc/2024/Conference | 2409.20012 | [
"https://github.com/haoyu-ha/lnln"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mY0ZnS2s9u | @inproceedings{
gao2024ddgsct,
title={{DDGS}-{CT}: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering},
author={Zhongpai Gao and Benjamin Planche and Meng Zheng and Xiao Chen and Terrence Chen and Ziyan Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mY0ZnS2s9u}
} | Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes, widely used in preoperative settings but limited in intraoperative applications due to computational bottlenecks. Physics-based Monte Carlo simulations provide accurate representations but are extremely computationally intensity. Analytical DRR renderers are much more efficient, but at the price of ignoring anisotropic X-ray image formation phenomena such as Compton scattering. We propose a novel approach that balances realistic physics-inspired X-ray simulation with efficient, differentiable DRR generation using 3D Gaussian splatting (3DGS). Our direction-disentangled 3DGS (DDGS) method decomposes the radiosity contribution into isotropic and direction-dependent components, able to approximate complex anisotropic interactions without complex runtime simulations. Additionally, we adapt the 3DGS initialization to account for tomography data properties, enhancing accuracy and efficiency. Our method outperforms state-of-the-art techniques in image accuracy and inference speed, demonstrating its potential for intraoperative applications and inverse problems like pose registration. | DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering | [
"Zhongpai Gao",
"Benjamin Planche",
"Meng Zheng",
"Xiao Chen",
"Terrence Chen",
"Ziyan Wu"
] | NeurIPS.cc/2024/Conference | 2406.02518 | [
""
] | https://huggingface.co/papers/2406.02518 | 1 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=mXpq6ut8J3 | @inproceedings{
yang2024sweagent,
title={{SWE}-agent: Agent-Computer Interfaces Enable Automated Software Engineering},
author={John Yang and Carlos E Jimenez and Alexander Wettig and Kilian Lieret and Shunyu Yao and Karthik R Narasimhan and Ofir Press},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mXpq6ut8J3}
} | Language model agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as integrated development environments, for complex tasks like software engineering, we posit that language model agents represent a new category of end users with their own needs and abilities, and would benefit from specially built interfaces to the software they use. We investigate how the role of interface design affects the performance of language model agents. As a result of this exploration, we introduce SWE-agent: a system that facilitates language model agents to autonomously use computers to solve software engineering tasks. SWE-agent's custom agent-computer interface significantly enhances an agent's ability to create and edit code files, navigate entire repositories, and execute tests and other programs. We evaluate SWE-agent on SWE-bench and HumanEvalFix, achieving state-of-the-art performance on both with a pass@1 rate of 12.5% and 87.7%, respectively, far exceeding the previous state-of-the-art achieved with non-interactive language models. Finally, we provide insight on how the design of the agent-computer interface can impact agents' behavior and performance. | SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering | [
"John Yang",
"Carlos E Jimenez",
"Alexander Wettig",
"Kilian Lieret",
"Shunyu Yao",
"Karthik R Narasimhan",
"Ofir Press"
] | NeurIPS.cc/2024/Conference | 2405.15793 | [
""
] | https://huggingface.co/papers/2405.15793 | 1 | 1 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=mXlR1FLFDc | @inproceedings{
wang2024a,
title={A Compositional Atlas for Algebraic Circuits},
author={Benjie Wang and Denis Mau{\'a} and Guy Van den Broeck and YooJung Choi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mXlR1FLFDc}
} | Circuits based on sum-product structure have become a ubiquitous representation to compactly encode knowledge, from Boolean functions to probability distributions. By imposing constraints on the structure of such circuits, certain inference queries become tractable, such as model counting and most probable configuration. Recent works have explored analyzing probabilistic and causal inference queries as compositions of basic operations to derive tractability conditions. In this paper, we take an algebraic perspective for compositional inference, and show that a large class of queries---including marginal MAP, probabilistic answer set programming inference, and causal backdoor adjustment---correspond to a combination of basic operations over semirings: aggregation, product, and elementwise mapping. Using this framework, we uncover simple and general sufficient conditions for tractable composition of these operations, in terms of circuit properties (e.g., marginal determinism, compatibility) and conditions on the elementwise mappings. Applying our analysis, we derive novel tractability conditions for many such compositional queries. Our results unify tractability conditions for existing problems on circuits, while providing a blueprint for analysing novel compositional inference queries. | A Compositional Atlas for Algebraic Circuits | [
"Benjie Wang",
"Denis Mauá",
"Guy Van den Broeck",
"YooJung Choi"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mVfRrMfGdY | @inproceedings{
schuchardt2024unified,
title={Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification},
author={Jan Schuchardt and Mihail Stoian and Arthur Kosmala and Stephan G{\"u}nnemann},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mVfRrMfGdY}
} | Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Renyi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results. | Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification | [
"Jan Schuchardt",
"Mihail Stoian",
"Arthur Kosmala",
"Stephan Günnemann"
] | NeurIPS.cc/2024/Conference | 2403.04867 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mTAbl8kUzq | @inproceedings{
sadat2024litevae,
title={Lite{VAE}: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models},
author={Seyedmorteza Sadat and Jakob Buhmann and Derek Bradley and Otmar Hilliges and Romann M. Weber},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mTAbl8kUzq}
} | Advances in latent diffusion models (LDMs) have revolutionized high-resolution image generation, but the design space of the autoencoder that is central to these systems remains underexplored. In this paper, we introduce LiteVAE, a new autoencoder design for LDMs, which leverages the 2D discrete wavelet transform to enhance scalability and computational efficiency over standard variational autoencoders (VAEs) with no sacrifice in output quality. We investigate the training methodologies and the decoder architecture of LiteVAE and propose several enhancements that improve the training dynamics and reconstruction quality. Our base LiteVAE model matches the quality of the established VAEs in current LDMs with a six-fold reduction in encoder parameters, leading to faster training and lower GPU memory requirements, while our larger model outperforms VAEs of comparable complexity across all evaluated metrics (rFID, LPIPS, PSNR, and SSIM). | LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models | [
"Seyedmorteza Sadat",
"Jakob Buhmann",
"Derek Bradley",
"Otmar Hilliges",
"Romann M. Weber"
] | NeurIPS.cc/2024/Conference | 2405.14477 | [
""
] | https://huggingface.co/papers/2405.14477 | 3 | 16 | 4 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=mSaqxZVZW8 | @inproceedings{
zhao2024seea,
title={SeeA*: Efficient Exploration-Enhanced A* Search by Selective Sampling},
author={Dengwei Zhao and Shikui Tu and Lei Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mSaqxZVZW8}
} | Monte-Carlo tree search (MCTS) and reinforcement learning contributed crucially to the success of AlphaGo and AlphaZero, and A$^*$ is a tree search algorithm among the most well-known ones in the classical AI literature. MCTS and A$^*$ both perform heuristic search and are mutually beneficial. Efforts have been made to the renaissance of A$^*$ from three possible aspects, two of which have been confirmed by studies in recent years, while the third is about the OPEN list that consists of open nodes of A$^*$ search, but still lacks deep investigation. This paper aims at the third, i.e., developing the Sampling-exploration enhanced A$^*$ (SeeA$^*$) search by constructing a dynamic subset of OPEN through a selective sampling process, such that the node with the best heuristic value in this subset instead of in the OPEN is expanded. Nodes with the best heuristic values in OPEN are most probably picked into this subset, but sometimes may not be included, which enables SeeA$^*$ to explore other promising branches. Three sampling techniques are presented for comparative investigations. Moreover, under the assumption about the distribution of prediction errors, we have theoretically shown the superior efficiency of SeeA$^*$ over A$^*$ search, particularly when the accuracy of the guiding heuristic function is insufficient. Experimental results on retrosynthetic planning in organic chemistry, logic synthesis in integrated circuit design, and the classical Sokoban game empirically demonstrate the efficiency of SeeA$^*$, in comparison with the state-of-the-art heuristic search algorithms. | SeeA*: Efficient Exploration-Enhanced A* Search by Selective Sampling | [
"Dengwei Zhao",
"Shikui Tu",
"Lei Xu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=mSHs6C7Nfa | @inproceedings{
lee2024improving,
title={Improving the Training of Rectified Flows},
author={Sangyun Lee and Zinan Lin and Giulia Fanti},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mSHs6C7Nfa}
} | Diffusion models have shown great promise for image and video generation, but sampling from state-of-the-art models requires expensive numerical integration of a generative ODE.
One approach for tackling this problem is rectified flows, which iteratively learn smooth ODE paths that are less susceptible to truncation error.
However, rectified flows still require a relatively large number of function evaluations (NFEs).
In this work, we propose improved techniques for training rectified flows, allowing them to compete with knowledge distillation methods even in the low NFE setting.
Our main insight is that under realistic settings, a single iteration of the Reflow algorithm for training rectified flows is sufficient to learn nearly straight trajectories; hence, the current practice of using multiple Reflow iterations is unnecessary.
We thus propose techniques to improve one-round training of rectified flows, including a U-shaped timestep distribution and LPIPS-Huber premetric.
With these techniques, we improve the FID of the previous 2-rectified flow by up to 75\% in the 1 NFE setting on CIFAR-10.
On ImageNet 64$\times$64, our improved rectified flow outperforms the state-of-the-art distillation methods
such as consistency distillation and progressive distillation in both one-step and two-step settings and rivals the performance of improved consistency training (iCT) in FID.
Code is available at https://github.com/sangyun884/rfpp. | Improving the Training of Rectified Flows | [
"Sangyun Lee",
"Zinan Lin",
"Giulia Fanti"
] | NeurIPS.cc/2024/Conference | 2405.20320 | [
"https://github.com/sangyun884/rfpp"
] | https://huggingface.co/papers/2405.20320 | 1 | 1 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=mRIQz8Zd6O | @inproceedings{
fu2024autoguide,
title={AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents},
author={Yao Fu and Dong-Ki Kim and Jaekyeom Kim and Sungryull Sohn and Lajanugen Logeswaran and Kyunghoon Bae and Honglak Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mRIQz8Zd6O}
} | Recent advances in large language models (LLMs) have empowered AI agents capable of performing various sequential decision-making tasks. However, effectively guiding LLMs to perform well in unfamiliar domains like web navigation, where they lack sufficient knowledge, has proven to be difficult with the demonstration-based in-context learning paradigm. In this paper, we introduce a novel framework, called AutoGuide, which addresses this limitation by automatically generating context-aware guidelines from offline experiences. Importantly, each context-aware guideline is expressed in concise natural language and follows a conditional structure, clearly describing the context where it is applicable. As a result, our guidelines facilitate the provision of relevant knowledge for the agent's current decision-making process, overcoming the limitations of the conventional demonstration-based learning paradigm. Our evaluation demonstrates that AutoGuide significantly outperforms competitive baselines in complex benchmark domains, including real-world web navigation. | AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents | [
"Yao Fu",
"Dong-Ki Kim",
"Jaekyeom Kim",
"Sungryull Sohn",
"Lajanugen Logeswaran",
"Kyunghoon Bae",
"Honglak Lee"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mP084aMFsd | @inproceedings{
li2024a,
title={A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences},
author={Mingjia Li and Shuo Liu and Hong Qian and Aimin Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mP084aMFsd}
} | In modern telecommunication networks, faults manifest as alarms, generating thousands of events daily. Network operators need an efficient method to identify the root causes of these alarms to mitigate potential losses. This task is challenging due to the increasing scale of telecommunication networks and the interconnected nature of devices, where one fault can trigger a cascade of alarms across multiple devices within a topological network. Recent years have seen a growing focus on causal approaches to addressing this problem, emphasizing the importance of learning a Granger causal graph from topological event sequences. Such causal graphs delineate the relations among alarms and can significantly aid engineers in identifying and rectifying faults. However, existing methods either ignore the topological relationships among devices or suffer from relatively low scalability and efficiency, failing to deliver high-quality responses in a timely manner. To this end, this paper proposes $S^2GCSL$, a simple yet scalable Granger causal structural learning approach for topological event sequences. $S^2GCSL$ utilizes a linear kernel to model activation interactions among various event types within a topological network, and employs gradient descent to efficiently optimize the likelihood function. Notably, it can seamlessly incorporate expert knowledge as constraints within the optimization process, which enhances the interpretability of the outcomes. Extensive experimental results on both large-scale synthetic and real-world problems verify the scalability and efficacy of $S^2GCSL$. | A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences | [
"Mingjia Li",
"Shuo Liu",
"Hong Qian",
"Aimin Zhou"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mOK4yD8JFd | @inproceedings{
zhou2024qualityimproved,
title={Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing},
author={Chu Zhou and Yixing Liu and Chao Xu and Boxin Shi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mOK4yD8JFd}
} | Polarimetric imaging is a challenging problem in the field of polarization-based vision, since setting a short exposure time reduces the signal-to-noise ratio, making the degree of polarization (DoP) and the angle of polarization (AoP) severely degenerated, while if setting a relatively long exposure time, the DoP and AoP would tend to be over-smoothed due to the frequently-occurring motion blur. This work proposes a polarimetric imaging framework that can produce clean and clear polarized snapshots by complementarily fusing a degraded pair of noisy and blurry ones. By adopting a neural network-based three-phase fusing scheme with specially-designed modules tailored to each phase, our framework can not only improve the image quality but also preserve the polarization properties. Experimental results show that our framework achieves state-of-the-art performance. | Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing | [
"Chu Zhou",
"Yixing Liu",
"Chao Xu",
"Boxin Shi"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mJZH9w8qgu | @inproceedings{
liu2024intrajectory,
title={In-Trajectory Inverse Reinforcement Learning: Learn Incrementally From An Ongoing Trajectory},
author={Shicheng Liu and Minghui Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mJZH9w8qgu}
} | Inverse reinforcement learning (IRL) aims to learn a reward function and a corresponding policy that best fit the demonstrated trajectories of an expert. However, current IRL works cannot learn incrementally from an ongoing trajectory because they have to wait to collect at least one complete trajectory to learn. To bridge the gap, this paper considers the problem of learning a reward function and a corresponding policy while observing the initial state-action pair of an ongoing trajectory and keeping updating the learned reward and policy when new state-action pairs of the ongoing trajectory are observed. We formulate this problem as an online bi-level optimization problem where the upper level dynamically adjusts the learned reward according to the newly observed state-action pairs with the help of a meta-regularization term, and the lower level learns the corresponding policy. We propose a novel algorithm to solve this problem and guarantee that the algorithm achieves sub-linear local regret $O(\sqrt{T}+\log T+\sqrt{T}\log T)$. If the reward function is linear, we prove that the proposed algorithm achieves sub-linear regret $O(\log T)$. Experiments are used to validate the proposed algorithm. | In-Trajectory Inverse Reinforcement Learning: Learn Incrementally From An Ongoing Trajectory | [
"Shicheng Liu",
"Minghui Zhu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mHtOyh5taj | @inproceedings{
zhu2024adaptive,
title={Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare},
author={Hanwei Zhu and Haoning Wu and Yixuan Li and Zicheng Zhang and Baoliang Chen and Lingyu Zhu and Yuming Fang and Guangtao Zhai and Weisi Lin and Shiqi Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mHtOyh5taj}
} | While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored. To address this gap, we introduce an all-around LMM-based NR-IQA model, which is capable of producing qualitatively comparative responses and effectively translating these discrete comparison outcomes into a continuous quality score. Specifically, during training, we present to generate scaled-up comparative instructions by comparing images from the same IQA dataset, allowing for more flexible integration of diverse IQA datasets. Utilizing the established large-scale training corpus, we develop a human-like visual quality comparator. During inference, moving beyond binary choices, we propose a soft comparison method that calculates the likelihood of the test image being preferred over multiple predefined anchor images. The quality score is further optimized by maximum a posteriori estimation with the resulting probability matrix. Extensive experiments on nine IQA datasets validate that the Compare2Score effectively bridges text-defined comparative levels during training with converted single image quality scores for inference, surpassing state-of-the-art IQA models across diverse scenarios. Moreover, we verify that the probability-matrix-based inference conversion not only improves the rating accuracy of Compare2Score but also zero-shot general-purpose LMMs, suggesting its intrinsic effectiveness. | Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare | [
"Hanwei Zhu",
"Haoning Wu",
"Yixuan Li",
"Zicheng Zhang",
"Baoliang Chen",
"Lingyu Zhu",
"Yuming Fang",
"Guangtao Zhai",
"Weisi Lin",
"Shiqi Wang"
] | NeurIPS.cc/2024/Conference | 2405.19298 | [
"https://github.com/Q-Future/Compare2Score"
] | https://huggingface.co/papers/2405.19298 | 0 | 0 | 0 | 10 | [
"q-future/Compare2Score",
"VQA-CityU/Compare2Score"
] | [] | [] | [
"q-future/Compare2Score",
"VQA-CityU/Compare2Score"
] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=mHVmsy9len | @inproceedings{
karhadkar2024bounds,
title={Bounds for the smallest eigenvalue of the {NTK} for arbitrary spherical data of arbitrary dimension},
author={Kedar Karhadkar and Michael Murray and Guido Montufar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mHVmsy9len}
} | Bounds on the smallest eigenvalue of the neural tangent kernel (NTK) are a key ingredient in the analysis of neural network optimization and memorization. However, existing results require distributional assumptions on the data and are limited to a high-dimensional setting, where the input dimension $d_0$ scales at least logarithmically in the number of samples $n$. In this work we remove both of these requirements and instead provide bounds in terms of a measure of distance between data points: notably these bounds hold with high probability even when $d_0$ is held constant versus $n$. We prove our results through a novel application of the hemisphere transform. | Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension | [
"Kedar Karhadkar",
"Michael Murray",
"Guido Montufar"
] | NeurIPS.cc/2024/Conference | 2405.14630 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mH1xtt2bJE | @inproceedings{
xie2024mano,
title={MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts},
author={RENCHUNZI XIE and Ambroise Odonnat and Vasilii Feofanov and Weijian Deng and Jianfeng Zhang and Bo An},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mH1xtt2bJE}
} | Leveraging the model’s outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples without requiring access to the corresponding ground-truth labels.
Despite their ease of implementation and computational efficiency, current logit-based methods are vulnerable to overconfidence issues, leading to prediction bias, especially under the natural shift. In this work, we first study the relationship between logits and generalization performance from the view of low-density separation assumption. Our findings motivate our proposed method \method{} that \textbf{(1)}~applies a data-dependent normalization on the logits to reduce prediction bias, and \textbf{(2)} takes the $L_p$ norm of the matrix of normalized logits as the estimation score. Our theoretical analysis highlights the connection between the provided score and the model's uncertainty.
We conduct an extensive empirical study on common unsupervised accuracy estimation benchmarks and demonstrate that \method{} achieves state-of-the-art performance across various architectures in the presence of synthetic, natural, or subpopulation shifts. The code is available at https://github.com/Renchunzi-Xie/MaNo. | MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts | [
"RENCHUNZI XIE",
"Ambroise Odonnat",
"Vasilii Feofanov",
"Weijian Deng",
"Jianfeng Zhang",
"Bo An"
] | NeurIPS.cc/2024/Conference | 2405.18979 | [
""
] | https://huggingface.co/papers/2405.18979 | 2 | 1 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=mGz3Jux9wS | @inproceedings{
duan2024longtailed,
title={Long-tailed Object Detection Pretraining: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction},
author={Chen-Long Duan and Yong Li and Xiu-Shen Wei and Lin Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mGz3Jux9wS}
} | Pre-training plays a vital role in various vision tasks, such as object recognition and detection. Commonly used pre-training methods, which typically rely on randomized approaches like uniform or Gaussian distributions to initialize model parameters, often fall short when confronted with long-tailed distributions, especially in detection tasks. This is largely due to extreme data imbalance and the issue of simplicity bias. In this paper, we introduce a novel pre-training framework for object detection, called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL). Our method builds on a Holistic-Local Contrastive Learning mechanism, which aligns pre-training with object detection by capturing both global contextual semantics and detailed local patterns. To tackle the imbalance inherent in long-tailed data, we design a dynamic rebalancing strategy that adjusts the sampling of underrepresented instances throughout the pre-training process, ensuring better representation of tail classes. Moreover, Dual Reconstruction addresses simplicity bias by enforcing a reconstruction task aligned with the self-consistency principle, specifically benefiting underrepresented tail classes. Experiments on COCO and LVIS v1.0 datasets demonstrate the effectiveness of our method, particularly in improving the mAP/AP scores for tail classes. | Long-tailed Object Detection Pretraining: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction | [
"Chen-Long Duan",
"Yong Li",
"Xiu-Shen Wei",
"Lin Zhao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mFrlCI8sov | @inproceedings{
var{\i}c{\i}2024interventional,
title={Interventional Causal Discovery in a Mixture of {DAG}s},
author={Burak Var{\i}c{\i} and Dmitriy A Katz and Dennis Wei and Prasanna Sattigeri and Ali Tajer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mFrlCI8sov}
} | Causal interactions among a group of variables are often modeled by a single causal graph. In some domains, however, these interactions are best described by multiple co-existing causal graphs, e.g., in dynamical systems or genomics. This paper addresses the hitherto unknown role of interventions in learning causal interactions among variables governed by a mixture of causal systems, each modeled by one directed acyclic graph (DAG). Causal discovery from mixtures is fundamentally more challenging than single-DAG causal discovery. Two major difficulties stem from (i) an inherent uncertainty about the skeletons of the component DAGs that constitute the mixture and (ii) possibly cyclic relationships across these component DAGs. This paper addresses these challenges and aims to identify edges that exist in at least one component DAG of the mixture, referred to as the *true* edges. First, it establishes matching necessary and sufficient conditions on the size of interventions required to identify the true edges. Next, guided by the necessity results, an adaptive algorithm is designed that learns all true edges using ${\cal O}(n^2)$ interventions, where $n$ is the number of nodes. Remarkably, the size of the interventions is optimal if the underlying mixture model does not contain cycles across its components. More generally, the gap between the intervention size used by the algorithm and the optimal size is quantified. It is shown to be bounded by the *cyclic complexity number* of the mixture model, defined as the size of the minimal intervention that can break the cycles in the mixture, which is upper bounded by the number of cycles among the ancestors of a node. | Interventional Causal Discovery in a Mixture of DAGs | [
"Burak Varıcı",
"Dmitriy A Katz",
"Dennis Wei",
"Prasanna Sattigeri",
"Ali Tajer"
] | NeurIPS.cc/2024/Conference | 2406.08666 | [
"https://github.com/bvarici/intervention-mixture-dag"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mCWZj7pa0M | @inproceedings{
holberg2024exact,
title={Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals},
author={Christian Holberg and Cristopher Salvi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mCWZj7pa0M}
} | We introduce a mathematically rigorous framework based on rough path theory to model stochastic spiking neural networks (SSNNs) as stochastic differential equations with event discontinuities (Event SDEs) and driven by càdlàg rough paths. Our formalism is general enough to allow for potential jumps to be present both in the solution trajectories as well as in the driving noise. We then identify a set of sufficient conditions ensuring the existence of pathwise gradients of solution trajectories and event times with respect to the network's parameters and show how these gradients satisfy a recursive relation. Furthermore, we introduce a general-purpose loss function defined by means of a new class of signature kernels indexed on càdlàg rough paths and use it to train SSNNs as generative models. We provide an end-to-end autodifferentiable solver for Event SDEs and make its implementation available as part of the $\texttt{diffrax}$ library. Our framework is, to our knowledge, the first enabling gradient-based training of SSNNs with noise affecting both the spike timing and the network's dynamics. | Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals | [
"Christian Holberg",
"Cristopher Salvi"
] | NeurIPS.cc/2024/Conference | 2405.13587 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=mAdGQ1Hh3L | @inproceedings{
guo2024start,
title={{START}: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation},
author={Jintao Guo and Lei Qi and Yinghuan Shi and Yang Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mAdGQ1Hh3L}
} | Domain Generalization (DG) aims to enable models to generalize to unseen target domains by learning from multiple source domains. Existing DG methods primarily rely on convolutional neural networks (CNNs), which inherently learn texture biases due to their limited receptive fields, making them prone to overfitting source domains. While some works have introduced transformer-based methods (ViTs) for DG to leverage the global receptive field, these methods incur high computational costs due to the quadratic complexity of self-attention. Recently, advanced state space models (SSMs), represented by Mamba, have shown promising results in supervised learning tasks by achieving linear complexity in sequence length during training and fast RNN-like computation during inference. Inspired by this, we investigate the generalization ability of the Mamba model under domain shifts and find that input-dependent matrices within SSMs could accumulate and amplify domain-specific features, thus hindering model generalization. To address this issue, we propose a novel SSM-based architecture with saliency-based token-aware transformation (namely START), which achieves state-of-the-art (SOTA) performances and offers a competitive alternative to CNNs and ViTs. Our START can selectively perturb and suppress domain-specific features in salient tokens within the input-dependent matrices of SSMs, thus effectively reducing the discrepancy between different domains. Extensive experiments on five benchmarks demonstrate that START outperforms existing SOTA DG methods with efficient linear complexity. Our code is available at https://github.com/lingeringlight/START. | START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation | [
"Jintao Guo",
"Lei Qi",
"Yinghuan Shi",
"Yang Gao"
] | NeurIPS.cc/2024/Conference | 2410.16020 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m9WZrEXWl5 | @inproceedings{
mishkin2024directional,
title={Directional Smoothness and Gradient Methods: Convergence and Adaptivity},
author={Aaron Mishkin and Ahmed Khaled and Yuanhao Wang and Aaron Defazio and Robert M. Gower},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m9WZrEXWl5}
} | We develop new sub-optimality bounds for gradient descent (GD) that depend on the conditioning of the objective along the path of optimization, rather than on global, worst-case constants. Key to our proofs is directional smoothness, a measure of gradient variation that we use to develop upper-bounds on the objective. Minimizing these upper-bounds requires solving implicit equations to obtain a sequence of strongly adapted step-sizes; we show that these equations are straightforward to solve for convex quadratics and lead to new guarantees for two classical step-sizes. For general functions, we prove that the Polyak step-size and normalized GD obtain fast, path-dependent rates despite using no knowledge of the directional smoothness. Experiments on logistic regression show our convergence guarantees are tighter than the classical theory based on $L$-smoothness. | Directional Smoothness and Gradient Methods: Convergence and Adaptivity | [
"Aaron Mishkin",
"Ahmed Khaled",
"Yuanhao Wang",
"Aaron Defazio",
"Robert M. Gower"
] | NeurIPS.cc/2024/Conference | 2403.04081 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m906PS5G9x | @inproceedings{
oliveira2024bayesian,
title={Bayesian Adaptive Calibration and Optimal Design},
author={Rafael Oliveira and Dino Sejdinovic and David Howard and Edwin V. Bonilla},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m906PS5G9x}
} | The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simulations and then calibrated against real observations. Current machine learning approaches, however, mostly rely on rerunning simulations over a fixed set of designs available in the observed data, potentially neglecting informative correlations across the design space and requiring a large amount of simulations. Instead, we consider the calibration process from the perspective of Bayesian adaptive experimental design and propose a data-efficient algorithm to run maximally informative simulations within a batch-sequential process. At each round, the algorithm jointly estimates the parameters posterior distribution and optimal designs by maximising a variational lower bound of the expected information gain. The simulator is modelled as a sample from a Gaussian process, which allows us to correlate simulations and real data with the unknown calibration parameters. We show the benefits of our method when compared to related approaches across synthetic and real-data problems. | Bayesian Adaptive Calibration and Optimal Design | [
"Rafael Oliveira",
"Dino Sejdinovic",
"David Howard",
"Edwin V. Bonilla"
] | NeurIPS.cc/2024/Conference | 2405.14440 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m8MElyzuwp | @inproceedings{
qi2024fetch,
title={Fetch and Forge: Efficient Dataset Condensation for Object Detection},
author={Ding Qi and Jian Li and Jinlong Peng and Bo Zhao and Shuguang Dou and Jialin Li and Jiangning Zhang and Yabiao Wang and Chengjie Wang and Cairong Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m8MElyzuwp}
} | Dataset condensation (DC) is an emerging technique capable of creating compact synthetic datasets from large originals while maintaining considerable performance. It is crucial for accelerating network training and reducing data storage requirements.
However, current research on DC mainly focuses on image classification, with less exploration of object detection.
This is primarily due to two challenges: (i) the multitasking nature of object detection complicates the condensation process, and (ii) Object detection datasets are characterized by large-scale and high-resolution data, which are difficult for existing DC methods to handle.
As a remedy, we propose DCOD, the first dataset condensation framework for object detection. It operates in two stages: Fetch and Forge, initially storing key localization and classification information into model parameters, and then reconstructing synthetic images via model inversion.
For the complex of multiple objects in an image, we propose Foreground Background Decoupling to centrally update the foreground of multiple instances and Incremental PatchExpand to further enhance the diversity of foregrounds.
Extensive experiments on various detection datasets demonstrate the superiority of DCOD. Even at an extremely low compression rate of 1\%, we achieve 46.4\% and 24.7\% $\text{AP}_{50}$ on the VOC and COCO, respectively, significantly reducing detector training duration. | Fetch and Forge: Efficient Dataset Condensation for Object Detection | [
"Ding Qi",
"Jian Li",
"Jinlong Peng",
"Bo Zhao",
"Shuguang Dou",
"Jialin Li",
"Jiangning Zhang",
"Yabiao Wang",
"Chengjie Wang",
"Cairong Zhao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=m6pVpdIN0y | @inproceedings{
dauphin2024neglected,
title={Neglected Hessian component explains mysteries in sharpness regularization},
author={Yann Dauphin and Atish Agarwala and Hossein Mobahi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m6pVpdIN0y}
} | Recent work has shown that methods that regularize second order information like SAM can improve generalization in deep learning. Seemingly similar methods like weight noise and gradient penalties often fail to provide such benefits. We investigate this inconsistency and reveal its connection to the the structure of the Hessian of the loss. Specifically, its decomposition into the positive semi-definite Gauss-Newton matrix and an indefinite matrix, which we call the Nonlinear Modeling Error (NME) matrix. Previous studies have largely overlooked the significance of the NME in their analysis for various reasons. However, we provide empirical and theoretical evidence that the NME is important to the performance of gradient penalties and explains their sensitivity to activation functions. We also provide evidence that the difference in regularization performance between gradient penalties and weight noise can be explained by the NME. Our findings emphasize the necessity of considering the NME in both experimental design and theoretical analysis for sharpness regularization. | Neglected Hessian component explains mysteries in sharpness regularization | [
"Yann Dauphin",
"Atish Agarwala",
"Hossein Mobahi"
] | NeurIPS.cc/2024/Conference | 2401.10809 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=m5dyKArVn8 | @inproceedings{
kim2024how,
title={How many classifiers do we need?},
author={Hyunsuk Kim and Liam Hodgkinson and Ryan Theisen and Michael W. Mahoney},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m5dyKArVn8}
} | As performance gains through scaling data and/or model size experience diminishing returns, it is becoming increasingly popular to turn to ensembling, where the predictions of multiple models are combined to improve accuracy.
In this paper, we provide a detailed analysis of how the disagreement and the polarization (a notion we introduce and define in this paper) among classifiers relate to the performance gain achieved by aggregating individual classifiers, for majority vote strategies in classification tasks.
We address these questions in the following ways.
(1) An upper bound for polarization is derived, and we propose what we call a neural polarization law: most interpolating neural network models are 4/3-polarized. Our empirical results not only support this conjecture but also show that polarization is nearly constant for a dataset, regardless of hyperparameters or architectures of classifiers.
(2) The error of the majority vote classifier is considered under restricted entropy conditions, and we present a tight upper bound that indicates that the disagreement is linearly correlated with the target, and that the slope is linear in the polarization.
(3) We prove results for the asymptotic behavior of the disagreement in terms of the number of classifiers, which we show can help in predicting the performance for a larger number of classifiers from that of a smaller number.
Our theories and claims are supported by empirical results on several image classification tasks with various types of neural networks. | How many classifiers do we need? | [
"Hyunsuk Kim",
"Liam Hodgkinson",
"Ryan Theisen",
"Michael W. Mahoney"
] | NeurIPS.cc/2024/Conference | 2411.00328 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m5CAnUui0Z | @inproceedings{
csaba2024label,
title={Label Delay in Online Continual Learning},
author={Botos Csaba and Wenxuan Zhang and Matthias M{\"u}ller and Ser-Nam Lim and Philip Torr and Adel Bibi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m5CAnUui0Z}
} | Online continual learning, the process of training models on streaming data, has gained increasing attention in recent years. However, a critical aspect often overlooked is the label delay, where new data may not be labeled due to slow and costly annotation processes. We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps. In each step, the framework reveals both unlabeled data from the current time step t and labels delayed with d steps, from the time step t−d. In our extensive experiments amounting to 1060 GPU days, we show that merely augmenting the computational resources is insufficient to tackle this challenge. Our findings underline a notable performance decline when solely relying on labeled data when the label delay becomes significant. More surprisingly, when using state-of-the-art SSL and TTA techniques to utilize the newer, unlabeled data, they fail to surpass the performance of a naïve method that simply trains on the delayed supervised stream. To this end, we introduce a simple, efficient baseline that rehearses from the labeled memory samples that are most similar to the new unlabeled samples. This method bridges the accuracy gap caused by label delay without significantly increasing computational complexity. We show experimentally that our method is the least affected by the label delay factor and in some cases successfully recovers the accuracy of the non-delayed counterpart. We conduct various ablations and sensitivity experiments, demonstrating the effectiveness of our approach. | Label Delay in Online Continual Learning | [
"Botos Csaba",
"Wenxuan Zhang",
"Matthias Müller",
"Ser-Nam Lim",
"Philip Torr",
"Adel Bibi"
] | NeurIPS.cc/2024/Conference | 2312.00923 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m5106RRLgx | @inproceedings{
chen2024are,
title={Are More {LLM} Calls All You Need? Towards the Scaling Properties of Compound {AI} Systems},
author={Lingjiao Chen and Jared Quincy Davis and Boris Hanin and Peter Bailis and Ion Stoica and Matei Zaharia and James Zou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m5106RRLgx}
} | Many recent state-of-the-art results in language tasks were achieved using compound systems that perform multiple Language Model (LM) calls and aggregate their responses. However, there is little understanding of how the number of LM calls -- e.g., when asking the LM to answer each question multiple times and taking a majority vote -- affects such a compound system's performance. In this paper, we initiate the study of scaling properties of compound inference systems. We analyze, theoretically and empirically, how the number of LM calls affects the performance of Vote and Filter-Vote, two of the simplest compound system designs, which aggregate LM responses via majority voting, optionally applying LM filters. We find, surprisingly, that across multiple language tasks, the performance of both Vote and Filter-Vote can first increase but then decrease as a function of the number of LM calls. Our theoretical results suggest that this non-monotonicity is due to the diversity of query difficulties within a task: more LM calls lead to higher performance on "easy" queries, but lower performance on "hard" queries, and non-monotone behavior can emerge when a task contains both types of queries. This insight then allows us to compute, from a small number of samples, the number of LM calls that maximizes system performance, and define an analytical scaling model for both systems. Experiments show that our scaling model can accurately predict the performance of Vote and Filter-Vote systems and thus find the optimal number of LM calls to make. | Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems | [
"Lingjiao Chen",
"Jared Quincy Davis",
"Boris Hanin",
"Peter Bailis",
"Ion Stoica",
"Matei Zaharia",
"James Zou"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=m4ZcDrVvid | @inproceedings{
cheng2024practical,
title={Practical Bayesian Algorithm Execution via Posterior Sampling},
author={Chu Xin Cheng and Raul Astudillo and Thomas Desautels and Yisong Yue},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m4ZcDrVvid}
} | We consider Bayesian algorithm execution (BAX), a framework for efficiently selecting evaluation points of an expensive function to infer a property of interest encoded as the output of a base algorithm. Since the base algorithm typically requires more evaluations than are feasible, it cannot be directly applied. Instead, BAX methods sequentially select evaluation points using a probabilistic numerical approach. Current BAX methods use expected information gain to guide this selection. However, this approach is computationally intensive. Observing that, in many tasks, the property of interest corresponds to a target set of points defined by the function, we introduce PS-BAX, a simple, effective, and scalable BAX method based on posterior sampling. PS-BAX is applicable to a wide range of problems, including many optimization variants and level set estimation. Experiments across diverse tasks demonstrate that PS-BAX performs competitively with existing baselines while being significantly faster, simpler to implement, and easily parallelizable, setting a strong baseline for future research. Additionally, we establish conditions under which PS-BAX is asymptotically convergent, offering new insights into posterior sampling as an algorithm design paradigm. | Practical Bayesian Algorithm Execution via Posterior Sampling | [
"Chu Xin Cheng",
"Raul Astudillo",
"Thomas Desautels",
"Yisong Yue"
] | NeurIPS.cc/2024/Conference | 2410.20596 | [
"https://github.com/RaulAstudillo06/PSBAX"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m4NI2yIwJA | @inproceedings{
jing2024deep,
title={Deep Graph Mating},
author={Yongcheng Jing and Seok-Hee Hong and Dacheng Tao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m4NI2yIwJA}
} | In this paper, we introduce the first learning-free model reuse task within the non-Euclidean domain, termed as Deep Graph Mating (Grama). We strive to create a child Graph Neural Network (GNN) that integrates knowledge from pre-trained parent models without requiring re-training, fine-tuning, or annotated labels. To this end, we begin by investigating the permutation invariance property of GNNs, which leads us to develop two vanilla approaches for Grama: Vanilla Parameter Interpolation (VPI) and Vanilla Alignment Prior to Interpolation (VAPI), both employing topology-independent interpolation in the parameter space. However, neither approach has achieved the anticipated results. Through theoretical analysis of VPI and VAPI, we identify critical challenges unique to Grama, including increased sensitivity to parameter misalignment and further the inherent topology-dependent complexities. Motivated by these findings, we propose the Dual-Message Coordination and Calibration (DuMCC) methodology, comprising the Parent Message Coordination (PMC) scheme to optimise the permutation matrices for parameter interpolation by coordinating aggregated messages, and the Child Message Calibration (CMC) scheme to mitigate over-smoothing identified in PMC by calibrating the message statistics within child GNNs. Experiments across diverse domains, including node and graph property prediction, 3D object recognition, and large-scale semantic parsing, demonstrate that the proposed DuMCC effectively enables training-free knowledge transfer, yielding results on par with those of pre-trained models. | Deep Graph Mating | [
"Yongcheng Jing",
"Seok-Hee Hong",
"Dacheng Tao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=m2DaXpCoIi | @inproceedings{
kunze2024practical,
title={Practical Shuffle Coding},
author={Julius Kunze and Daniel Severo and Jan-Willem van de Meent and James Townsend},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m2DaXpCoIi}
} | We present a general method for lossless compression of unordered data structures, including multisets and graphs. It is a variant of shuffle coding that is many orders of magnitude faster than the original and enables 'one-shot' compression of single unordered objects. Our method achieves state-of-the-art compression rates on various large-scale network graphs at speeds of megabytes per second, efficiently handling even a multi-gigabyte plain graph with one billion edges. We release an implementation that can be easily adapted to different data types and statistical models. | Practical Shuffle Coding | [
"Julius Kunze",
"Daniel Severo",
"Jan-Willem van de Meent",
"James Townsend"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=m296WJXyzQ | @inproceedings{
mirzaei2024scanning,
title={Scanning Trojaned Models Using Out-of-Distribution Samples},
author={Hossein Mirzaei and Ali Ansari and Bahar Dibaei Nia and Mojtaba Nafez and Moein Madadi and Sepehr Rezaee and Zeinab Sadat Taghavi and Arad Maleki and Kian Shamsaie and Mahdi Hajialilue and Jafar Habibi and Mohammad Sabokrou and Mohammad Hossein Rohban},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m296WJXyzQ}
} | Scanning for trojan (backdoor) in deep neural networks is crucial due to their significant real-world applications. There has been an increasing focus on developing effective general trojan scanning methods across various trojan attacks. Despite advancements, there remains a shortage of methods that perform effectively without preconceived assumptions about the backdoor attack method. Additionally, we have observed that current methods struggle to identify classifiers trojaned using adversarial training. Motivated by these challenges, our study introduces a novel scanning method named TRODO (TROjan scanning by Detection of adversarial shifts in Out-of-distribution samples). TRODO leverages the concept of "blind spots"—regions where trojaned classifiers erroneously identify out-of-distribution (OOD) samples as in-distribution (ID). We scan for these blind spots by adversarially shifting OOD samples towards in-distribution. The increased likelihood of perturbed OOD samples being classified as ID serves as a signature for trojan detection. TRODO is both trojan and label mapping agnostic, effective even against adversarially trained trojaned classifiers. It is applicable even in scenarios where training data is absent, demonstrating high accuracy and adaptability across various scenarios and datasets, highlighting its potential as a robust trojan scanning strategy. | Scanning Trojaned Models Using Out-of-Distribution Samples | [
"Hossein Mirzaei",
"Ali Ansari",
"Bahar Dibaei Nia",
"Mojtaba Nafez",
"Moein Madadi",
"Sepehr Rezaee",
"Zeinab Sadat Taghavi",
"Arad Maleki",
"Kian Shamsaie",
"Mahdi Hajialilue",
"Jafar Habibi",
"Mohammad Sabokrou",
"Mohammad Hossein Rohban"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=m1a4CrRJR7 | @inproceedings{
zhang2024generalization,
title={Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure},
author={Jin Zhang and Ze Liu and Defu Lian and Enhong Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m1a4CrRJR7}
} | Two-stage recommender systems play a crucial role in efficiently identifying relevant items and personalizing recommendations from a vast array of options. This paper, based on an error decomposition framework, analyzes the generalization error for two-stage recommender systems with a tree structure, which consist of an efficient tree-based retriever and a more precise yet time-consuming ranker. We use the Rademacher complexity to establish the generalization upper bound for various tree-based retrievers using beam search, as well as for different ranker models under a shifted training distribution. Both theoretical insights and practical experiments on real-world datasets indicate that increasing the branches in tree-based retrievers and harmonizing distributions across stages can enhance the generalization performance of two-stage recommender systems. | Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure | [
"Jin Zhang",
"Ze Liu",
"Defu Lian",
"Enhong Chen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=m1PVjNHvtP | @inproceedings{
zeng2024glinsat,
title={{GL}in{SAT}: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent},
author={Hongtai Zeng and Chao Yang and Yanzhen Zhou and Cheng Yang and Qinglai Guo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m1PVjNHvtP}
} | Ensuring that the outputs of neural networks satisfy specific constraints is crucial for applying neural networks to real-life decision-making problems. In this paper, we consider making a batch of neural network outputs satisfy bounded and general linear constraints. We first reformulate the neural network output projection problem as an entropy-regularized linear programming problem. We show that such a problem can be equivalently transformed into an unconstrained convex optimization problem with Lipschitz continuous gradient according to the duality theorem. Then, based on an accelerated gradient descent algorithm with numerical performance enhancement, we present our architecture, GLinSAT, to solve the problem. To the best of our knowledge, this is the first general linear satisfiability layer in which all the operations are differentiable and matrix-factorization-free. Despite the fact that we can explicitly perform backpropagation based on automatic differentiation mechanism, we also provide an alternative approach in GLinSAT to calculate the derivatives based on implicit differentiation of the optimality condition. Experimental results on constrained traveling salesman problems, partial graph matching with outliers, predictive portfolio allocation and power system unit commitment demonstrate the advantages of GLinSAT over existing satisfiability layers. Our implementation is available at https://github.com/HunterTracer/GLinSAT. | GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent | [
"Hongtai Zeng",
"Chao Yang",
"Yanzhen Zhou",
"Cheng Yang",
"Qinglai Guo"
] | NeurIPS.cc/2024/Conference | 2409.17500 | [
"https://github.com/huntertracer/glinsat"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m0jZUvlKl7 | @inproceedings{
ji2024arpro,
title={{AR}-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties},
author={Xiayan Ji and Anton Xue and Eric Wong and Oleg Sokolsky and Insup Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m0jZUvlKl7}
} | Anomaly detection is widely used for identifying critical errors and suspicious behaviors, but current methods lack interpretability.
We leverage common properties of existing methods and recent advances in generative models to introduce counterfactual explanations for anomaly detection.
Given an input, we generate its counterfactual as a diffusion-based repair that shows what a non-anomalous version $\textit{should have looked like}$.
A key advantage of this approach is that it enables a domain-independent formal specification of explainability desiderata, offering a unified framework for generating and evaluating explanations.
We demonstrate the effectiveness of our anomaly explainability framework, AR-Pro, on vision (MVTec, VisA) and time-series (SWaT, WADI, HAI) anomaly datasets. The code used for the experiments is accessible at: https://github.com/xjiae/arpro. | AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties | [
"Xiayan Ji",
"Anton Xue",
"Eric Wong",
"Oleg Sokolsky",
"Insup Lee"
] | NeurIPS.cc/2024/Conference | 2410.24178 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=m0DS4OOmSY | @inproceedings{
li2024should,
title={Should We Really Edit Language Models? On the Evaluation of Edited Language Models},
author={Qi Li and Xiang Liu and Zhenheng Tang and Peijie Dong and Zeyu Li and Xinglin Pan and Xiaowen Chu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m0DS4OOmSY}
} | Model editing has become an increasingly popular alternative for efficiently updating knowledge within language models.
Current methods mainly focus on reliability, generalization, and locality, with many methods excelling across these criteria.
Some recent works disclose the pitfalls of these editing methods such as knowledge distortion or conflict. However, the general abilities of post-edited language models remain unexplored.
In this paper, we perform a comprehensive evaluation on various editing methods and different language models, and have following findings.
(1) Existing editing methods lead to inevitable performance deterioration on general benchmarks, indicating that existing editing methods maintain the general abilities of the model within only a few dozen edits.
When the number of edits is slightly large, the intrinsic knowledge structure of the model is disrupted or even completely damaged.
(2) Instruction-tuned models are more robust to editing, showing less performance drop on general knowledge after editing.
(3) Language model with large scale is more resistant to editing compared to small model.
(4) The safety of the edited model, is significantly weakened, even for those safety-aligned models.
Our findings indicate that current editing methods are only suitable for small-scale knowledge updates within language models, which motivates further research on more practical and reliable editing methods. | Should We Really Edit Language Models? On the Evaluation of Edited Language Models | [
"Qi Li",
"Xiang Liu",
"Zhenheng Tang",
"Peijie Dong",
"Zeyu Li",
"Xinglin Pan",
"Xiaowen Chu"
] | NeurIPS.cc/2024/Conference | 2410.18785 | [
"https://github.com/lqinfdim/editingevaluation"
] | https://huggingface.co/papers/2410.18785 | 1 | 5 | 2 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=lzfzjYuWgY | @inproceedings{
li2024do,
title={Do {LLM}s Build World Representations? Probing Through the Lens of State Abstraction},
author={Zichao Li and Yanshuai Cao and Jackie CK Cheung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lzfzjYuWgY}
} | How do large language models (LLMs) encode the state of the world, including the status of entities and their relations, as described by a text? While existing work directly probes for a complete state of the world, our research explores whether and how LLMs abstract this world state in their internal representations. We propose a new framework for probing for world representations through the lens of state abstraction theory from reinforcement learning, which emphasizes different levels of abstraction, distinguishing between general abstractions that facilitate predicting future states and goal-oriented abstractions that guide the subsequent actions to accomplish tasks. To instantiate this framework, we design a text-based planning task, where an LLM acts as an agent in an environment and interacts with objects in containers to achieve a specified goal state. Our experiments reveal that fine-tuning as well as advanced pre-training strengthens LLM-built representations' tendency of maintaining goal-oriented abstractions during decoding, prioritizing task completion over recovery of the world's state and dynamics. | Do LLMs Build World Representations? Probing Through the Lens of State Abstraction | [
"Zichao Li",
"Yanshuai Cao",
"Jackie CK Cheung"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lxuXvJSOcP | @inproceedings{
chang2024unified,
title={Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection},
author={Gyusam Chang and Jiwon Lee and Donghyun Kim and Jinkyu Kim and Dongwook Lee and Daehyun Ji and Sujin Jang and Sangpil Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lxuXvJSOcP}
} | Recent advances in 3D object detection leveraging multi-view cameras have demonstrated their practical and economical value in various challenging vision tasks.
However, typical supervised learning approaches face challenges in achieving satisfactory adaptation toward unseen and unlabeled target datasets (i.e., direct transfer) due to the inevitable geometric misalignment between the source and target domains.
In practice, we also encounter constraints on resources for training models and collecting annotations for the successful deployment of 3D object detectors.
In this paper, we propose Unified Domain Generalization and Adaptation (UDGA), a practical solution to mitigate those drawbacks.
We first propose Multi-view Overlap Depth Constraint that leverages the strong association between multi-view, significantly alleviating geometric gaps due to perspective view changes.
Then, we present a Label-Efficient Domain Adaptation approach to handle unfamiliar targets with significantly fewer amounts of labels (i.e., 1$\%$ and 5$\%)$, while preserving well-defined source knowledge for training efficiency.
Overall, UDGA framework enables stable detection performance in both source and target domains, effectively bridging inevitable domain gaps, while demanding fewer annotations.
We demonstrate the robustness of UDGA with large-scale benchmarks: nuScenes, Lyft, and Waymo, where our framework outperforms the current state-of-the-art methods. | Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection | [
"Gyusam Chang",
"Jiwon Lee",
"Donghyun Kim",
"Jinkyu Kim",
"Dongwook Lee",
"Daehyun Ji",
"Sujin Jang",
"Sangpil Kim"
] | NeurIPS.cc/2024/Conference | 2410.22461 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lxhoVDf1Sw | @inproceedings{
mounir2024predictive,
title={Predictive Attractor Models},
author={Ramy Mounir and Sudeep Sarkar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lxhoVDf1Sw}
} | Sequential memory, the ability to form and accurately recall a sequence of events or stimuli in the correct order, is a fundamental prerequisite for biological and artificial intelligence as it underpins numerous cognitive functions (e.g., language comprehension, planning, episodic memory formation, etc.) However, existing methods of sequential memory suffer from catastrophic forgetting, limited capacity, slow iterative learning procedures, low-order Markov memory, and, most importantly, the inability to represent and generate multiple valid future possibilities stemming from the same context. Inspired by biologically plausible neuroscience theories of cognition, we propose Predictive Attractor Models (PAM), a novel sequence memory architecture with desirable generative properties. PAM is a streaming model that learns a sequence in an online, continuous manner by observing each input only once. Additionally, we find that PAM avoids catastrophic forgetting by uniquely representing past context through lateral inhibition in cortical minicolumns, which prevents new memories from overwriting previously learned knowledge. PAM generates future predictions by sampling from a union set of predicted possibilities; this generative ability is realized through an attractor model trained alongside the predictor. We show that PAM is trained with local computations through Hebbian plasticity rules in a biologically plausible framework. Other desirable traits (e.g., noise tolerance, CPU-based learning, capacity scaling) are discussed throughout the paper. Our findings suggest that PAM represents a significant step forward in the pursuit of biologically plausible and computationally efficient sequential memory models, with broad implications for cognitive science and artificial intelligence research. Illustration videos and code are available on our project page: https://ramymounir.com/publications/pam. | Predictive Attractor Models | [
"Ramy Mounir",
"Sudeep Sarkar"
] | NeurIPS.cc/2024/Conference | 2410.02430 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lxSmLxlVks | @inproceedings{
shen2024search,
title={Search for Efficient Large Language Models},
author={Xuan Shen and Pu Zhao and Yifan Gong and Zhenglun Kong and Zheng Zhan and Yushu Wu and Ming Lin and Chao Wu and Xue Lin and Yanzhi Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lxSmLxlVks}
} | Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research.
Numerous efficient techniques, including weight pruning, quantization, and distillation, have been embraced to compress LLMs, targeting memory reduction and inference acceleration, which underscore the redundancy in LLMs.
However, most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
Besides, traditional architecture search methods, limited by the elevated complexity with extensive parameters, struggle to demonstrate their effectiveness on LLMs.
In this paper, we propose a training-free architecture search framework to identify optimal subnets that preserve the fundamental strengths of the original LLMs while achieving inference acceleration.
Furthermore, after generating subnets that inherit specific weights from the original LLMs, we introduce a reformation algorithm that utilizes the omitted weights to rectify the inherited weights with a small amount of calibration data.
Compared with SOTA training-free structured pruning works that can generate smaller networks, our method demonstrates superior performance across standard benchmarks.
Furthermore, our generated subnets can directly reduce the usage of GPU memory and achieve inference acceleration. | Search for Efficient Large Language Models | [
"Xuan Shen",
"Pu Zhao",
"Yifan Gong",
"Zhenglun Kong",
"Zheng Zhan",
"Yushu Wu",
"Ming Lin",
"Chao Wu",
"Xue Lin",
"Yanzhi Wang"
] | NeurIPS.cc/2024/Conference | 2409.17372 | [
"https://github.com/shawnricecake/search-llm"
] | https://huggingface.co/papers/2409.17372 | 0 | 0 | 0 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=lwpfH9wVkO | @inproceedings{
adams2024controlling,
title={Controlling Multiple Errors Simultaneously with a {PAC}-Bayes Bound},
author={Reuben Adams and John Shawe-Taylor and Benjamin Guedj},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lwpfH9wVkO}
} | Current PAC-Bayes generalisation bounds are restricted to scalar metrics of performance, such as the loss or error rate. However, one ideally wants more information-rich certificates that control the entire distribution of possible outcomes, such as the distribution of the test loss in regression, or the probabilities of different mis-classifications. We provide the first PAC-Bayes bound capable of providing such rich information by bounding the Kullback-Leibler divergence between the empirical and true probabilities of a set of $M$ error types, which can either be discretized loss values for regression, or the elements of the confusion matrix (or a partition thereof) for classification. We transform our bound into a differentiable training objective. Our bound is especially useful in cases where the severity of different mis-classifications may change over time; existing PAC-Bayes bounds can only bound a particular pre-decided weighting of the error types. In contrast our bound implicitly controls all uncountably many weightings simultaneously. | Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound | [
"Reuben Adams",
"John Shawe-Taylor",
"Benjamin Guedj"
] | NeurIPS.cc/2024/Conference | 2202.05560 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lvibangnAs | @inproceedings{
zhou2024unifying,
title={Unifying Generation and Prediction on Graphs with Latent Graph Diffusion},
author={Cai Zhou and Xiyuan Wang and Muhan Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lvibangnAs}
} | In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classification) using one formulation. We first formulate prediction tasks including regression and classification into a generic (conditional) generation framework, which enables diffusion models to perform deterministic tasks with provable guarantees. We then propose Latent Graph Diffusion (LGD), a generative model that can generate node, edge, and graph-level features of all categories simultaneously. We achieve this goal by embedding the graph structures and features into a latent space leveraging a powerful encoder and decoder, then training a diffusion model in the latent space. LGD is also capable of conditional generation through a specifically designed cross-attention mechanism. Leveraging LGD and the ``all tasks as generation'' formulation, our framework is capable of solving graph tasks of various levels and types. We verify the effectiveness of our framework with extensive experiments, where our models achieve state-of-the-art or highly competitive results across a wide range of generation and regression tasks. | Unifying Generation and Prediction on Graphs with Latent Graph Diffusion | [
"Cai Zhou",
"Xiyuan Wang",
"Muhan Zhang"
] | NeurIPS.cc/2024/Conference | 2402.02518 | [
"https://github.com/zhouc20/latentgraphdiffusion"
] | https://huggingface.co/papers/2402.02518 | 0 | 1 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=lvcWA24dxB | @inproceedings{
montanaro2024motioncraft,
title={MotionCraft: Physics-Based Zero-Shot Video Generation},
author={Antonio Montanaro and Luca Savant Aira and Emanuele Aiello and Diego Valsesia and Enrico Magli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lvcWA24dxB}
} | Generating videos with realistic and physically plausible motion is one of the main recent challenges in computer vision.
While diffusion models are achieving compelling results in image generation, video diffusion models are limited by heavy training and huge models, resulting in videos that are still biased to the training dataset. In this work we propose MotionCraft, a new zero-shot video generator to craft physics-based and realistic videos. MotionCraft is able to warp the noise latent space of an image diffusion model, such as Stable Diffusion, by applying an optical flow derived from a physics simulation. We show that warping the noise latent space results in coherent application of the desired motion while allowing the model to generate missing elements consistent with the scene evolution, which would otherwise result in artefacts or missing content if the flow was applied in the pixel space.
We compare our method with the state-of-the-art Text2Video-Zero reporting qualitative and quantitative improvements, demonstrating the effectiveness of our approach to generate videos with finely-prescribed complex motion dynamics. | MotionCraft: Physics-Based Zero-Shot Video Generation | [
"Antonio Montanaro",
"Luca Savant Aira",
"Emanuele Aiello",
"Diego Valsesia",
"Enrico Magli"
] | NeurIPS.cc/2024/Conference | 2405.13557 | [
"https://github.com/mezzelfo/MotionCraft"
] | https://huggingface.co/papers/2405.13557 | 2 | 1 | 0 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=lvS2b8CjG5 | @inproceedings{
wang2024eegpt,
title={{EEGPT}: Pretrained Transformer for Universal and Reliable Representation of {EEG} Signals},
author={Guangyu Wang and Wenchao Liu and Yuhong He and Cong Xu and Lin Ma and Haifeng Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lvS2b8CjG5}
} | Electroencephalography (EEG) is crucial for recording brain activity,
with applications in medicine, neuroscience, and brain-computer interfaces (BCI).
However, challenges such as low signal-to-noise ratio (SNR), high inter-subject variability, and channel mismatch complicate the extraction of robust,
universal EEG representations.
We propose EEGPT, a novel 10-million-parameter pretrained transformer model designed for universal EEG feature extraction.
In EEGPT, a mask-based dual self-supervised learning method for efficient feature extraction is designed.
Compared to other mask-based self-supervised learning methods,
EEGPT introduces spatio-temporal representation alignment.
This involves constructing a self-supervised task based on
EEG representations that possess high SNR and rich semantic information,
rather than on raw signals.
Consequently, this approach mitigates the issue of poor feature quality typically
extracted from low SNR signals.
Additionally, EEGPT's hierarchical structure processes spatial and temporal information separately,
reducing computational complexity while increasing flexibility and adaptability for BCI applications.
By training on a large mixed multi-task EEG dataset, we fully exploit EEGPT's capabilities.
The experiment validates the efficacy and scalability of EEGPT,
achieving state-of-the-art performance on a range of downstream tasks with linear-probing.
Our research advances EEG representation learning, offering innovative solutions for bio-signal processing and AI applications.
The code for this paper is available at: https://github.com/BINE022/EEGPT | EEGPT: Pretrained Transformer for Universal and Reliable Representation of EEG Signals | [
"Guangyu Wang",
"Wenchao Liu",
"Yuhong He",
"Cong Xu",
"Lin Ma",
"Haifeng Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=luQiVmnviX | @inproceedings{
zhou2024unibias,
title={UniBias: Unveiling and Mitigating {LLM} Bias through Internal Attention and {FFN} Manipulation},
author={Hanzhang Zhou and Zijian Feng and Zixiao Zhu and Junlang Qian and Kezhi Mao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=luQiVmnviX}
} | Large language models (LLMs) have demonstrated impressive capabilities in various tasks using the in-context learning (ICL) paradigm. However, their effectiveness is often compromised by inherent bias, leading to prompt brittleness—sensitivity to design settings such as example selection, order, and prompt formatting. Previous studies have addressed LLM bias through external adjustment of model outputs, but the internal mechanisms that lead to such bias remain unexplored. Our work delves into these mechanisms, particularly investigating how feedforward neural networks (FFNs) and attention heads result in the bias of LLMs. By Interpreting the contribution of individual FFN vectors and attention heads, we identify the biased LLM components that skew LLMs' prediction toward specific labels. To mitigate these biases, we introduce UniBias, an inference-only method that effectively identifies and eliminates biased FFN vectors and attention heads. Extensive experiments across 12 NLP datasets demonstrate that UniBias significantly enhances ICL performance and alleviates prompt brittleness of LLMs. | UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation | [
"Hanzhang Zhou",
"Zijian Feng",
"Zixiao Zhu",
"Junlang Qian",
"Kezhi Mao"
] | NeurIPS.cc/2024/Conference | 2405.20612 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ltnDg0EzF9 | @inproceedings{
zhang2024latent,
title={Latent Intrinsics Emerge from Training to Relight},
author={Xiao Zhang and William Gao and Seemandhar Jain and Michael Maire and David Forsyth and Anand Bhattad},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ltnDg0EzF9}
} | Image relighting is the task of showing what a scene from a source image would look like if illuminated differently. Inverse graphic schemes recover an explicit representation of geometry and a set of chosen intrinsics, then relight with some form of renderer. But error control for inverse graphics is difficult, and inverse graphics methods can represent only the effects of the chosen intrinsics. This paper describes a relighting method that is entirely data-driven, where intrinsics and lighting are each represented as latent variables. Our approach produces SOTA relightings of real scenes, as measured by standard metrics. We show that albedo can be recovered from our latent intrinsics without using any example albedos, and that the albedos recovered are competitive with SOTA methods. | Latent Intrinsics Emerge from Training to Relight | [
"Xiao Zhang",
"William Gao",
"Seemandhar Jain",
"Michael Maire",
"David Forsyth",
"Anand Bhattad"
] | NeurIPS.cc/2024/Conference | 2405.21074 | [
""
] | https://huggingface.co/papers/2405.21074 | 1 | 1 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=lsd27JUJ8v | @inproceedings{
zhang2024exploiting,
title={Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical {MDP} Iteration},
author={Hongming Zhang and Chenjun Xiao and Chao Gao and Han Wang and bo xu and Martin M{\"u}ller},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lsd27JUJ8v}
} | Reinforcement learning (RL) algorithms are typically based on optimizing a Markov Decision Process (MDP) using the optimal Bellman equation. Recent studies have revealed that focusing the optimization of Bellman equations solely on in-sample actions tends to result in more stable optimization, especially in the presence of function approximation. Upon on these findings, in this paper, we propose an Empirical MDP Iteration (EMIT) framework. EMIT constructs a sequence of empirical MDPs using data from the growing replay memory. For each of these empirical MDPs, it learns an estimated Q-function denoted as $\widehat{Q}$. The key strength is that by restricting the Bellman update to in-sample bootstrapping, each empirical MDP converges to a unique optimal $\widehat{Q}$ function. Furthermore, gradually expanding from the empirical MDPs to the original MDP induces a monotonic policy improvement. Instead of creating entirely new algorithms, we demonstrate that EMIT can be seamlessly integrated with existing online RL algorithms, effectively acting as a regularizer for contemporary Q-learning methods. We show this by implementing EMIT for two representative RL algorithms, DQN and TD3. Experimental results on Atari and MuJoCo benchmarks show that EMIT significantly reduces estimation errors and substantially improves the performance of both algorithms. | Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration | [
"Hongming Zhang",
"Chenjun Xiao",
"Chao Gao",
"Han Wang",
"bo xu",
"Martin Müller"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lrSrJZZCle | @inproceedings{
ou2024coda,
title={{CODA}: A Correlation-Oriented Disentanglement and Augmentation Modeling Scheme for Better Resisting Subpopulation Shifts},
author={Ziquan OU and Zijun Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lrSrJZZCle}
} | Data-driven models learned often struggle to generalize due to widespread subpopulation shifts, especially the presence of both spurious correlations and group imbalance (SC-GI). To learn models more powerful for defending against SC-GI, we propose a {\bf Correlation-Oriented Disentanglement and Augmentation (CODA)} modeling scheme, which includes two unique developments: (1) correlation-oriented disentanglement and (2) strategic sample augmentation with reweighted consistency (RWC) loss. In (1), a bi-branch encoding process is developed to enable the disentangling of variant and invariant correlations by coordinating with a decoy classifier and the decoder reconstruction. In (2), a strategic sample augmentation based on disentangled latent features with RWC loss is designed to reinforce the training of a more generalizable model. The effectiveness of CODA is verified by benchmarking against a set of SOTA models in terms of worst-group accuracy and maximum group accuracy gap based on two famous datasets, ColoredMNIST and CelebA. | CODA: A Correlation-Oriented Disentanglement and Augmentation Modeling Scheme for Better Resisting Subpopulation Shifts | [
"Ziquan OU",
"Zijun Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lpxdG0hk4H | @inproceedings{
yang2024showmaker,
title={ShowMaker: Creating High-Fidelity 2D Human Video via Fine-Grained Diffusion Modeling},
author={Quanwei Yang and Jiazhi Guan and Kaisiyuan Wang and Lingyun Yu and Wenqing Chu and Hang Zhou and ZhiQiang Feng and Haocheng Feng and Errui Ding and Jingdong Wang and Hongtao Xie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lpxdG0hk4H}
} | Although significant progress has been made in human video generation, most previous studies focus on either human facial animation or full-body animation, which cannot be directly applied to produce realistic conversational human videos with frequent hand gestures and various facial movements simultaneously.
To address these limitations, we propose a 2D human video generation framework, named ShowMaker, capable of generating high-fidelity half-body conversational videos via fine-grained diffusion modeling.
We leverage dual-stream diffusion models as the backbone of our framework and carefully design two novel components for crucial local regions (i.e., hands and face) that can be easily integrated into our backbone.
Specifically, to handle the challenging hand generation caused by sparse motion guidance, we propose a novel Key Point-based Fine-grained Hand Modeling module by amplifying positional information from raw hand key points and constructing a corresponding key point-based codebook.
Moreover, to restore richer facial details in generated results, we introduce a Face Recapture module, which extracts facial texture features and global identity features from the aligned human face and integrates them into the diffusion process for face enhancement.
Extensive quantitative and qualitative experiments demonstrate the superior visual quality and temporal consistency of our method. | ShowMaker: Creating High-Fidelity 2D Human Video via Fine-Grained Diffusion Modeling | [
"Quanwei Yang",
"Jiazhi Guan",
"Kaisiyuan Wang",
"Lingyun Yu",
"Wenqing Chu",
"Hang Zhou",
"ZhiQiang Feng",
"Haocheng Feng",
"Errui Ding",
"Jingdong Wang",
"Hongtao Xie"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lpXDZKiAnt | @inproceedings{
huang2024vaccine,
title={Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack},
author={Tiansheng Huang and Sihao Hu and Ling Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lpXDZKiAnt}
} | The new paradigm of fine-tuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the fine-tuning to produce an alignment-broken model. We conduct an empirical analysis and uncover
a \textit{harmful embedding drift} phenomenon, showing a probable
cause of the alignment-broken effect. Inspired by our findings, we propose Vaccine, a perturbation-aware alignment technique to mitigate the security risk of users fine-tuning. The core idea of Vaccine is to produce invariant hidden embeddings by progressively adding crafted perturbation to them in the alignment phase. This enables the embeddings to withstand harmful perturbation from un-sanitized user data in the fine-tuning phase. Our results on open source mainstream LLMs (e.g., Llama2, Opt, Vicuna) demonstrate that Vaccine can boost the robustness of alignment against harmful prompts induced embedding drift while reserving reasoning ability towards benign prompts. Our code is available at https://github.com/git-disl/Vaccine. | Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack | [
"Tiansheng Huang",
"Sihao Hu",
"Ling Liu"
] | NeurIPS.cc/2024/Conference | 2402.01109 | [
"https://github.com/git-disl/vaccine"
] | https://huggingface.co/papers/2402.01109 | 1 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=lpFDhC91Oj | @inproceedings{
kuwana2024blackbox,
title={Black-Box Forgetting},
author={Yusuke Kuwana and Yuta Goto and Takashi Shibata and Go Irie},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lpFDhC91Oj}
} | Large-scale pre-trained models (PTMs) provide remarkable zero-shot classification capability covering a wide variety of object classes. However, practical applications do not always require the classification of all kinds of objects, and leaving the model capable of recognizing unnecessary classes not only degrades overall accuracy but also leads to operational disadvantages. To mitigate this issue, we explore the selective forgetting problem for PTMs, where the task is to make the model unable to recognize only the specified classes, while maintaining accuracy for the rest. All the existing methods assume ''white-box'' settings, where model information such as architectures, parameters, and gradients is available for training. However, PTMs are often ''black-box,'' where information on such models is unavailable for commercial reasons or social responsibilities. In this paper, we address a novel problem of selective forgetting for black-box models, named Black-Box Forgetting, and propose an approach to the problem. Given that information on the model is unavailable, we optimize the input prompt to decrease the accuracy of specified classes through derivative-free optimization. To avoid difficult high-dimensional optimization while ensuring high forgetting performance, we propose Latent Context Sharing, which introduces common low-dimensional latent components among multiple tokens for the prompt. Experiments on four standard benchmark datasets demonstrate the superiority of our method with reasonable baselines. The code is available at https://github.com/yusukekwn/Black-Box-Forgetting. | Black-Box Forgetting | [
"Yusuke Kuwana",
"Yuta Goto",
"Takashi Shibata",
"Go Irie"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=loQCk0qruU | @inproceedings{
pandey2024be,
title={Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models},
author={Deep Shankar Pandey and Spandan Pyakurel and Qi Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=loQCk0qruU}
} | Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance. Parameter efficient fine-tuning ($\texttt{PEFT}$) provides promising generalization performance in adaptation while incurring minimum computational overhead. However, adaptation of these foundation models through $\texttt{PEFT}$ leads to accurate but severely underconfident models, especially in few-shot learning settings. Moreover, the adapted models lack accurate fine-grained uncertainty quantification capabilities limiting their broader applicability in critical domains. To fill out this critical gap, we develop a novel lightweight {Bayesian Parameter Efficient Fine-Tuning} (referred to as $\texttt{Bayesian-PEFT}$) framework for large transformer-based foundation models. The framework integrates state-of-the-art $\texttt{PEFT}$ techniques with two Bayesian components to address the under-confidence issue while ensuring reliable prediction under challenging few-shot settings. The first component performs base rate adjustment to strengthen the prior belief corresponding to the knowledge gained through pre-training, making the model more confident in its predictions; the second component builds an evidential ensemble that leverages belief regularization to ensure diversity among different ensemble components.
Our thorough theoretical analysis justifies that the Bayesian components can ensure reliable and accurate few-shot adaptations with well-calibrated uncertainty quantification. Extensive experiments across diverse datasets, few-shot learning scenarios, and multiple $\texttt{PEFT}$ techniques demonstrate the outstanding prediction and calibration performance by $\texttt{Bayesian-PEFT}$. | Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models | [
"Deep Shankar Pandey",
"Spandan Pyakurel",
"Qi Yu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=loMa99A4p8 | @inproceedings{
sahoo2024diffusion,
title={Diffusion Models With Learned Adaptive Noise},
author={Subham Sekhar Sahoo and Aaron Gokaslan and Christopher De Sa and Volodymyr Kuleshov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=loMa99A4p8}
} | Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images. Central to these algorithms is the diffusion process, a set of equations which maps data to noise
in a way that can significantly affect performance.
In this paper, we explore whether the diffusion
process can be learned from data.
Our work is grounded in Bayesian inference and seeks to improve log-likelihood estimation by casting the learned diffusion process as an approximate variational posterior that yields a tighter lower bound (ELBO) on the likelihood.
A widely held assumption is that the ELBO is invariant to the noise process: our work dispels this assumption and proposes multivariate learned adaptive noise (MuLAN), a learned diffusion process that applies noise at different rates across an image. Our method consists of three components: a multivariate noise schedule, adaptive input-conditional diffusion, and auxiliary variables; these components ensure that the ELBO is no longer invariant to the choice of the noise schedule as in previous works. Empirically, MuLAN sets a new **state-of-the-art** in density estimation on CIFAR-10 and ImageNet while matching the performance of previous state-of-the-art models with **50%** fewer steps. We provide the code, along with a blog post and video tutorial on the project page: https://s-sahoo.com/MuLAN | Diffusion Models With Learned Adaptive Noise | [
"Subham Sekhar Sahoo",
"Aaron Gokaslan",
"Christopher De Sa",
"Volodymyr Kuleshov"
] | NeurIPS.cc/2024/Conference | 2312.13236 | [
"https://github.com/s-sahoo/mulan"
] | https://huggingface.co/papers/2312.13236 | 0 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=lmsCSDymEP | @inproceedings{
li2024humanobject,
title={Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models},
author={Liulei Li and Wenguan Wang and Yi Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lmsCSDymEP}
} | Prevalent human-object interaction (HOI) detection approaches typically leverage large-scale visual-linguistic models to help recognize events involving humans and objects. Though promising, models trained via contrastive learning on text-image pairs often neglect mid/low-level visual cues and struggle at compositional reasoning. In response, we introduce DIFFUSIONHOI, a new HOI detector shedding light on text-to-image diffusion models. Unlike the aforementioned models, diffusion models excel in discerning mid/low-level visual concepts as generative models, and possess strong compositionality to handle novel concepts expressed in text inputs. Considering diffusion models usually emphasize instance objects, we first devise an inversion-based strategy to learn the expression of relation patterns between humans and objects in embedding space. These learned relation embeddings then serve as textual prompts, to steer diffusion models generate images that depict specific interactions, and extract HOI-relevant cues from images without heavy finetuning. Benefited from above, DIFFUSIONHOI achieves SOTA performance on three datasets under both regular and zero-shot setups. | Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models | [
"Liulei Li",
"Wenguan Wang",
"Yi Yang"
] | NeurIPS.cc/2024/Conference | 2410.20155 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=llTroju97T | @inproceedings{
chen2024personalized,
title={Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models},
author={Shengchao Chen and Guodong Long and Jing Jiang and Chengqi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=llTroju97T}
} | This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variable modeling. We present LM-Weather, a generic approach to taming PLMs, that have learned massive sequential knowledge from the universe of natural language databases, to acquire an immediate capability to obtain highly customized models for heterogeneous meteorological data on devices while keeping high efficiency. Concretely, we introduce a lightweight personalized adapter into PLMs and endows it with weather pattern awareness. During communication between clients and the server, low-rank-based transmission is performed to effectively fuse the global knowledge among devices while maintaining high communication efficiency and ensuring privacy. Experiments on real-wold dataset show that LM-Weather outperforms the state-of-the-art results by a large margin across various tasks (e.g., forecasting and imputation at different scales). We provide extensive and in-depth analyses experiments, which verify that LM-Weather can (1) indeed leverage sequential knowledge from natural language to accurately handle meteorological sequence, (2) allows each devices obtain highly customized models under significant heterogeneity, and (3) generalize under data-limited and out-of-distribution (OOD) scenarios. | Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models | [
"Shengchao Chen",
"Guodong Long",
"Jing Jiang",
"Chengqi Zhang"
] | NeurIPS.cc/2024/Conference | 2405.20348 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lkx3OpcqSZ | @inproceedings{
saha2024compressing,
title={Compressing Large Language Models using Low Rank and Low Precision Decomposition},
author={Rajarshi Saha and Naomi Sagan and Varun Srivastava and Andrea Goldsmith and Mert Pilanci},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lkx3OpcqSZ}
} | The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces $\rm CALDERA$ -- a new post-training LLM compression algorithm that harnesses the inherent low-rank structure of a weight matrix $\mathbf{W}$ by approximating it via a low-rank, low-precision decomposition as $\mathbf{W} \approx \mathbf{Q} + \mathbf{L}\mathbf{R}$. Here, $\mathbf{L}$ and $\mathbf{R}$ are low rank factors, and the entries of $\mathbf{Q}$, $\mathbf{L}$ and $\mathbf{R}$ are quantized. The model is compressed by substituting each layer with its $\mathbf{Q} + \mathbf{L}\mathbf{R}$ decomposition, and the zero-shot performance of the compressed model is evaluated. Additionally, $\mathbf{L}$ and $\mathbf{R}$ are readily amenable to low-rank adaptation, consequently enhancing the zero-shot performance. $\rm CALDERA$ obtains this decomposition by formulating it as an optimization problem $\min_{\mathbf{Q},\mathbf{L},\mathbf{R}}\lVert(\mathbf{Q} + \mathbf{L}\mathbf{R} - \mathbf{W})\mathbf{X}^\top\rVert_{\rm F}^2$, where $\mathbf{X}$ is the calibration data, and $\mathbf{Q}, \mathbf{L}, \mathbf{R}$ are constrained to be representable using low-precision formats. Theoretical upper bounds on the approximation error of $\rm CALDERA$ are established using a rank-constrained regression framework, and the tradeoff between compression ratio and model performance is studied by analyzing the impact of target rank and quantization bit budget. Results illustrate that compressing LlaMa-$2$ $7$B/$13$B/$70$B and LlaMa-$3$ $8$B models obtained using $\rm CALDERA$ outperforms existing post-training LLM compression techniques in the regime of less than $2.5$ bits per parameter. | Compressing Large Language Models using Low Rank and Low Precision Decomposition | [
"Rajarshi Saha",
"Naomi Sagan",
"Varun Srivastava",
"Andrea Goldsmith",
"Mert Pilanci"
] | NeurIPS.cc/2024/Conference | 2405.18886 | [
"https://github.com/pilancilab/caldera"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=liHe9iumIi | @inproceedings{
yin2024fewviewgs,
title={FewView{GS}: Gaussian Splatting with Few View Matching and Multi-stage Training},
author={Ruihong Yin and Vladimir Yugay and Yue Li and Sezer Karaoglu and Theo Gevers},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=liHe9iumIi}
} | The field of novel view synthesis from images has seen rapid advancements with the introduction of Neural Radiance Fields (NeRF) and more recently with 3D Gaussian Splatting. Gaussian Splatting became widely adopted due to its efficiency and ability to render novel views accurately. While Gaussian Splatting performs well when a sufficient amount of training images are available, its unstructured explicit representation tends to overfit in scenarios with sparse input images, resulting in poor rendering performance. To address this, we present a 3D Gaussian-based novel view synthesis method using sparse input images that can accurately render the scene from the viewpoints not covered by the training images. We propose a multi-stage training scheme with matching-based consistency constraints imposed on the novel views without relying on pre-trained depth estimation or diffusion models. This is achieved by using the matches of the available training images to supervise the generation of the novel views sampled between the training frames with color, geometry, and semantic losses. In addition, we introduce a locality preserving regularization for 3D Gaussians which removes rendering artifacts by preserving the local color structure of the scene. Evaluation on synthetic and real-world datasets demonstrates competitive or superior performance of our method in few-shot novel view synthesis compared to existing state-of-the-art methods. | FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training | [
"Ruihong Yin",
"Vladimir Yugay",
"Yue Li",
"Sezer Karaoglu",
"Theo Gevers"
] | NeurIPS.cc/2024/Conference | 2411.02229 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lhlIUxD5eE | @inproceedings{
chao2024maximum,
title={Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow},
author={Chen-Hao Chao and Chien Feng and Wei-Fang Sun and Cheng-Kuang Lee and Simon See and Chun-Yi Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lhlIUxD5eE}
} | Existing Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) methods for continuous action spaces are typically formulated based on actor-critic frameworks and optimized through alternating steps of policy evaluation and policy improvement. In the policy evaluation steps, the critic is updated to capture the soft Q-function. In the policy improvement steps, the actor is adjusted in accordance with the updated soft Q-function. In this paper, we introduce a new MaxEnt RL framework modeled using Energy-Based Normalizing Flows (EBFlow). This framework integrates the policy evaluation steps and the policy improvement steps, resulting in a single objective training process. Our method enables the calculation of the soft value function used in the policy evaluation target without Monte Carlo approximation. Moreover, this design supports the modeling of multi-modal action distributions while facilitating efficient action sampling. To evaluate the performance of our method, we conducted experiments on the MuJoCo benchmark suite and a number of high-dimensional robotic tasks simulated by Omniverse Isaac Gym. The evaluation results demonstrate that our method achieves superior performance compared to widely-adopted representative baselines. | Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow | [
"Chen-Hao Chao",
"Chien Feng",
"Wei-Fang Sun",
"Cheng-Kuang Lee",
"Simon See",
"Chun-Yi Lee"
] | NeurIPS.cc/2024/Conference | 2405.13629 | [
"https://github.com/ChienFeng-hub/meow"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lgtsXxk4dF | @inproceedings{
black2024clustering,
title={Clustering with Non-adaptive Subset Queries},
author={Hadley Black and Euiwoong Lee and Arya Mazumdar and Barna Saha},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lgtsXxk4dF}
} | Recovering the underlying clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the last decade. Given a query $S \subset U$, $|S|=2$, the oracle returns "yes" if the points are in the same cluster and "no" otherwise. We study a natural generalization of this problem to subset queries for $|S|>2$, where the oracle returns the number of clusters intersecting $S$. Our aim is to determine the minimum number of queries needed for exactly recovering an arbitrary $k$-clustering. We focus on non-adaptive schemes, where all the queries are asked in one round, thus allowing for the querying process to be parallelized, which is a highly desirable property.
For adaptive algorithms with pair-wise queries, the complexity is known to be $\Theta(nk)$, where $k$ is the number of clusters.
In contrast, non-adaptive pair-wise query algorithms are extremely limited: even for $k=3$, such algorithms require $\Omega(n^2)$ queries, which matches the trivial $O(n^2)$ upper bound attained by querying every pair of points. Allowing for subset queries of unbounded size, $O(n)$ queries is possible with an adaptive scheme. However, the realm of non-adaptive algorithms remains completely unknown. Is it possible to attain algorithms that are non-adaptive while still making a near-linear number of queries?
In this paper, we give the first non-adaptive algorithms for clustering with subset queries. We provide, (i) a non-adaptive algorithm making $O(n \log^2 n \log k)$ queries which improves to $O(n \log k)$ when the cluster sizes are within any constant factor of each other, (ii) for constant $k$, a non-adaptive algorithm making $O(n \log{\log{n}})$ queries. In addition to non-adaptivity, we take into account other practical considerations, such as enforcing a bound on query size. For constant $k$, we give an algorithm making $\smash{\widetilde{O}(n^2/s^2)}$ queries on subsets of size at most $s \leq \sqrt{n}$, which is optimal among all non-adaptive algorithms within a $\log n$-factor. For arbitrary $k$, the dependence varies as $\tilde{O}(n^2/s)$. | Clustering with Non-adaptive Subset Queries | [
"Hadley Black",
"Euiwoong Lee",
"Arya Mazumdar",
"Barna Saha"
] | NeurIPS.cc/2024/Conference | 2409.10908 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lfxIASyLxB | @inproceedings{
collins2024incontext,
title={In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness},
author={Liam Collins and Advait U Parulekar and Aryan Mokhtari and sujay sanghavi and Sanjay Shakkottai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lfxIASyLxB}
} | A striking property of transformers is their ability to perform in-context learning (ICL), a machine learning framework in which the learner is presented with a novel context during inference implicitly through some data, and tasked with making a prediction in that context. As such, that learner must adapt to the context without additional training. We explore the role of *softmax* attention in an ICL setting where each context encodes a regression task. We show that an attention unit learns a window that it uses to implement a nearest-neighbors predictor adapted to the landscape of the pretraining tasks. Specifically, we show that this window widens with decreasing Lipschitzness and increasing label noise in the pretraining tasks. We also show that on low-rank, linear problems, the attention unit learns to project onto the appropriate subspace before inference. Further, we show that this adaptivity relies crucially on the softmax activation and thus cannot be replicated by the linear activation often studied in prior theoretical analyses. | In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness | [
"Liam Collins",
"Advait U Parulekar",
"Aryan Mokhtari",
"sujay sanghavi",
"Sanjay Shakkottai"
] | NeurIPS.cc/2024/Conference | 2402.11639 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=lflwtGE6Vf | @inproceedings{
lee2024fl,
title={({FL})\${\textasciicircum}2\$: Overcoming Few Labels in Federated Semi-Supervised Learning},
author={Seungjoo Lee and Thanh-Long V. Le and Jaemin Shin and Sung-Ju Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lflwtGE6Vf}
} | Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches assume that clients possess labeled data, which is often not the case in practice. Federated Semi-Supervised Learning (FSSL) addresses this label deficiency problem, targeting situations where only the server has a small amount of labeled data while clients do not. However, a significant performance gap exists between Centralized Semi-Supervised Learning (SSL) and FSSL. This gap arises from confirmation bias, which is more pronounced in FSSL due to multiple local training epochs and the separation of labeled and unlabeled data. We propose $(FL)^2$, a robust training method for unlabeled clients using sharpness-aware consistency regularization. We show that regularizing the original pseudo-labeling loss is suboptimal, and hence we carefully select unlabeled samples for regularization. We further introduce client-specific adaptive thresholding and learning status-aware aggregation to adjust the training process based on the learning progress of each client. Our experiments on three benchmark datasets demonstrate that our approach significantly improves performance and bridges the gap with SSL, particularly in scenarios with scarce labeled data. | (FL)^2: Overcoming Few Labels in Federated Semi-Supervised Learning | [
"Seungjoo Lee",
"Thanh-Long V. Le",
"Jaemin Shin",
"Sung-Ju Lee"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/seungjoo-ai/FLFL-NeurIPS24"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lfY0SUT3m9 | @inproceedings{
tran-dinh2024shuffling,
title={Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization},
author={Quoc Tran-Dinh and Trang H. Tran and Lam M. Nguyen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lfY0SUT3m9}
} | This paper aims at developing novel shuffling gradient-based methods for tackling two classes of minimax problems: nonconvex-linear and nonconvex-strongly concave settings. The first algorithm addresses the nonconvex-linear minimax model and achieves the state-of-the-art oracle complexity typically observed in nonconvex optimization. It also employs a new shuffling estimator for the ``hyper-gradient'', departing from standard shuffling techniques in optimization. The second method consists of two variants: semi-shuffling and full-shuffling schemes. These variants tackle the nonconvex-strongly concave minimax setting. We establish their oracle complexity bounds under standard assumptions, which, to our best knowledge, are the best-known for this specific setting. Numerical examples demonstrate the performance of our algorithms and compare them with two other methods. Our results show that the new methods achieve comparable performance with SGD, supporting the potential of incorporating shuffling strategies into minimax algorithms. | Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization | [
"Quoc Tran-Dinh",
"Trang H. Tran",
"Lam M. Nguyen"
] | NeurIPS.cc/2024/Conference | 2410.22297 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=leqD3bJ4Ly | @inproceedings{
chowdhury2024opel,
title={{OPEL}: Optimal Transport Guided ProcedurE Learning},
author={Sayeed Shafayet Chowdhury and Soumyadeep Chandra and Kaushik Roy},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=leqD3bJ4Ly}
} | Procedure learning refers to the task of identifying the key-steps and determining their logical order, given several videos of the same task. For both third-person and first-person (egocentric) videos, state-of-the-art (SOTA) methods aim at finding correspondences across videos in time to accomplish procedure learning. However, to establish temporal relationships within the sequences, these methods often rely on frame-to-frame mapping, or assume monotonic alignment of video pairs, leading to sub-optimal results. To this end, we propose to treat the video frames as samples from an unknown distribution, enabling us to frame their distance calculation as an optimal transport (OT) problem. Notably, the OT-based formulation allows us to relax the previously mentioned assumptions. To further improve performance, we enhance the OT formulation by introducing two regularization terms. The first, inverse difference moment regularization, promotes transportation between instances that are homogeneous in the embedding space as well as being temporally closer. The second, regularization based on the KL-divergence with an exponentially decaying prior smooths the alignment while enforcing conformity to the optimality (alignment obtained from vanilla OT optimization) and temporal priors. The resultant optimal transport guided procedure learning framework (`OPEL') significantly outperforms the SOTA on benchmark datasets. Specifically, we achieve 22.4\% (IoU) and 26.9\% (F1) average improvement compared to the current SOTA on large scale egocentric benchmark, EgoProceL. Furthermore, for the third person benchmarks (ProCeL and CrossTask), the proposed approach obtains 46.2\% (F1) average enhancement over SOTA. | OPEL: Optimal Transport Guided ProcedurE Learning | [
"Sayeed Shafayet Chowdhury",
"Soumyadeep Chandra",
"Kaushik Roy"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=leeosk2RAM | @inproceedings{
li2024searchlvlms,
title={Search{LVLM}s: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge},
author={Chuanhao Li and Zhen Li and Chenchen Jing and Shuo Liu and Wenqi Shao and Yuwei Wu and Ping Luo and Yu Qiao and Kaipeng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=leeosk2RAM}
} | Large vision-language models (LVLMs) are ignorant of the up-to-date knowledge, such as LLaVA series, because they cannot be updated frequently due to the large amount of resources required, and therefore fail in many cases. For example, if a LVLM was released on January 2024, and it wouldn't know the singer of the theme song for the new Detective Conan movie, which wasn't released until April 2024. To solve the problem, a promising solution motivated by retrieval-augmented generation (RAG) is to provide LVLMs with up-to-date knowledge via internet search during inference, i.e., internet-augmented generation (IAG), which is already integrated in some closed-source commercial LVLMs such as GPT-4V. However, the specific mechanics underpinning them remain a mystery. In this paper, we propose a plug-and-play framework, for augmenting existing LVLMs in handling visual question answering (VQA) about up-to-date knowledge, dubbed SearchLVLMs. A hierarchical filtering model is trained to effectively and efficiently find the most helpful content from the websites returned by a search engine to prompt LVLMs with up-to-date knowledge. To train the model and evaluate our framework's performance, we propose a pipeline to automatically generate news-related VQA samples to construct a dataset, dubbed UDK-VQA. A multi-model voting mechanism is introduced to label the usefulness of website/content for VQA samples to construct the training set. Experimental results demonstrate the effectiveness of our framework, outperforming GPT-4o by $\sim$30\% in accuracy. | SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge | [
"Chuanhao Li",
"Zhen Li",
"Chenchen Jing",
"Shuo Liu",
"Wenqi Shao",
"Yuwei Wu",
"Ping Luo",
"Yu Qiao",
"Kaipeng Zhang"
] | NeurIPS.cc/2024/Conference | 2405.14554 | [
""
] | https://huggingface.co/papers/2405.14554 | 1 | 0 | 0 | 9 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=ldvfaYzG35 | @inproceedings{
wang2024pedestriancentric,
title={Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective},
author={MeiJun Wang and Yu Meng and Zhongwei Qiu and Chao Zheng and Yan Xu and Pengxiaorui and Jian Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ldvfaYzG35}
} | Pedestrian pre-collision pose is one of the key factors to determine the degree of pedestrian-vehicle injury in collision. Human pose estimation algorithm is an effective method to estimate pedestrian emergency pose from accident video. However, the pose estimation model trained by the existing daily human pose datasets has poor robustness under specific poses such as pedestrian pre-collision pose, and it is difficult to obtain human pose datasets in the wild scenes, especially lacking scarce data such as pedestrian pre-collision pose in traffic scenes. In this paper, we collect pedestrian-vehicle collision pose from the dashcam perspective of dashcam and construct the first Pedestrian-Vehicle Collision Pose dataset (PVCP) in a semi-automatic way, including 40k+ accident frames and 20K+ pedestrian pre-collision pose annotation (2D, 3D, Mesh). Further, we construct a Pedestrian Pre-collision Pose Estimation Network (PPSENet) to estimate the collision pose and shape sequence of pedestrians from pedestrian-vehicle accident videos. The PPSENet first estimates the 2D pose from the image (Image to Pose, ITP) and then lifts the 2D pose to 3D mesh (Pose to Mesh, PTM). Due to the small size of the dataset, we introduce a pre-training model that learns the human pose prior on a large number of pose datasets, and use iterative regression to estimate the pre-collision pose and shape of pedestrians. Further, we classify the pre-collision pose sequence and introduce pose class loss, which achieves the best accuracy compared with the existing relevant \textit{state-of-the-art} methods. Code and data are available for research at https://github.com/wmj142326/PVCP. | Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective | [
"MeiJun Wang",
"Yu Meng",
"Zhongwei Qiu",
"Chao Zheng",
"Yan Xu",
"Pengxiaorui",
"Jian Gao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ldXyNSvXEr | @inproceedings{
ge2024optimal,
title={Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift},
author={Jiawei Ge and Debarghya Mukherjee and Jianqing Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ldXyNSvXEr}
} | As machine learning models are increasingly deployed in dynamic environments, it becomes paramount to assess and quantify uncertainties associated with distribution shifts.
A distribution shift occurs when the underlying data-generating process changes, leading to a deviation in the model's performance.
The prediction interval, which captures the range of likely outcomes for a given prediction, serves as a crucial tool for characterizing uncertainties induced by their underlying distribution.
In this paper, we propose methodologies for aggregating prediction intervals to obtain one with minimal width and adequate coverage on the target domain under unsupervised domain shift, under which we have labeled samples from a related source domain and unlabeled covariates from the target domain.
Our analysis encompasses scenarios where the source and the target domain are related via i) a bounded density ratio, and ii) a measure-preserving transformation.
Our proposed methodologies are computationally efficient and easy to implement. Beyond illustrating the performance of our method through real-world datasets, we also delve into the theoretical details. This includes establishing rigorous theoretical guarantees, coupled with finite sample bounds, regarding the coverage and width of our prediction intervals. Our approach excels in practical applications and is underpinned by a solid theoretical framework, ensuring its reliability and effectiveness across diverse contexts. | Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift | [
"Jiawei Ge",
"Debarghya Mukherjee",
"Jianqing Fan"
] | NeurIPS.cc/2024/Conference | 2405.10302 | [
"https://github.com/jiaweige0416/pi-with-shift"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lckAdnVzsT | @inproceedings{
dahnert2024coherent,
title={Coherent 3D Scene Diffusion From a Single {RGB} Image},
author={Manuel Dahnert and Angela Dai and Norman M{\"u}ller and Matthias Nie{\ss}ner},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lckAdnVzsT}
} | We present a novel diffusion-based approach for coherent 3D scene reconstruction from a single RGB image.
Our method utilizes an image-conditioned 3D scene diffusion model to simultaneously denoise the 3D poses and geometries of all objects within the scene.
Motivated by the ill-posed nature of the task and to obtain consistent scene reconstruction results, we learn a generative scene prior by conditioning on all scene objects simultaneously to capture scene context and by allowing the model to learn inter-object relationships throughout the diffusion process.
We further propose an efficient surface alignment loss to facilitate training even in the absence of full ground-truth annotation, which is common in publicly available datasets. This loss leverages an expressive shape representation, which enables direct point sampling from intermediate shape predictions.
By framing the task of single RGB image 3D scene reconstruction as a conditional diffusion process, our approach surpasses current state-of-the-art methods, achieving a 12.04\% improvement in AP3D on SUN RGB-D and a 13.43\% increase in F-Score on Pix3D. | Coherent 3D Scene Diffusion From a Single RGB Image | [
"Manuel Dahnert",
"Angela Dai",
"Norman Müller",
"Matthias Nießner"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=lcALCNF2qe | @inproceedings{
zhang2024towards,
title={Towards Universal Mesh Movement Networks},
author={Mingrui Zhang and Chunyang Wang and Stephan C. Kramer and Joseph Gregory Wallwork and Siyi Li and Jiancheng Liu and Xiang Chen and Matthew D Piggott},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lcALCNF2qe}
} | Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movement methods provide the capability to improve the accuracy of the numerical solution without increasing the overall mesh degree of freedom count. Conventional sophisticated mesh movement methods are extremely expensive and struggle to handle scenarios with complex boundary geometries. However, existing learning-based methods require re-training from scratch given a different PDE type or boundary geometry, which limits their applicability, and also often suffer from robustness issues in the form of inverted elements. In this paper, we introduce the Universal Mesh Movement Network (UM2N), which -- once trained -- can be applied in a non-intrusive, zero-shot manner to move meshes with different size distributions and structures, for solvers applicable to different PDE types and boundary geometries. UM2N consists of a Graph Transformer (GT) encoder for extracting features and a Graph Attention Network (GAT) based decoder for moving the mesh. We evaluate our method on advection and Navier-Stokes based examples, as well as a real-world tsunami simulation case. Our method out-performs existing learning-based mesh movement methods in terms of the benchmarks described above. In comparison to the conventional sophisticated Monge-Ampère PDE-solver based method, our approach not only significantly accelerates mesh movement, but also proves effective in scenarios where the conventional method fails. Our project page can be found at https://erizmr.github.io/UM2N/. | Towards Universal Mesh Movement Networks | [
"Mingrui Zhang",
"Chunyang Wang",
"Stephan C. Kramer",
"Joseph Gregory Wallwork",
"Siyi Li",
"Jiancheng Liu",
"Xiang Chen",
"Matthew D Piggott"
] | NeurIPS.cc/2024/Conference | 2407.00382 | [
"https://github.com/mesh-adaptation/um2n"
] | https://huggingface.co/papers/2407.00382 | 2 | 0 | 0 | 8 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=lbSI1j8m6p | @inproceedings{
cui2024automated,
title={Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records},
author={Suhan Cui and Prasenjit Mitra},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lbSI1j8m6p}
} | In the realm of big data and digital healthcare, Electronic Health Records (EHR) have become a rich source of information with the potential to improve patient care and medical research. In recent years, machine learning models have proliferated for analyzing EHR data to predict patients' future health conditions. Among them, some studies advocate for multi-task learning (MTL) to jointly predict multiple target diseases for improving the prediction performance over single task learning. Nevertheless, current MTL frameworks for EHR data have significant limitations due to their heavy reliance on human experts to identify task groups for joint training and design model architectures. To reduce human intervention and improve the framework design, we propose an automated approach named AutoDP, which can search for the optimal configuration of task grouping and architectures simultaneously. To tackle the vast joint search space encompassing task combinations and architectures, we employ surrogate model-based optimization, enabling us to efficiently discover the optimal solution. Experimental results on real-world EHR data demonstrate the efficacy of the proposed AutoDP framework. It achieves significant performance improvements over both hand-crafted and automated state-of-the-art methods, also maintains a feasible search cost at the same time. | Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records | [
"Suhan Cui",
"Prasenjit Mitra"
] | NeurIPS.cc/2024/Conference | 2403.04086 | [
"https://github.com/sh-src/autodp"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lbLC5OV9GY | @inproceedings{
zimmermann2024visa,
title={{VISA}: Variational Inference with Sequential Sample-Average Approximations},
author={Heiko Zimmermann and Christian A. Naesseth and Jan-Willem van de Meent},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lbLC5OV9GY}
} | We present variational inference with sequential sample-average approximations (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations. VISA extends importance-weighted forward-KL variational inference by employing a sequence of sample-average approximations, which are considered valid inside a trust region. This makes it possible to reuse model evaluations across multiple gradient steps, thereby reducing computational cost. We perform experiments on high-dimensional Gaussians, Lotka-Volterra dynamics, and a Pickover attractor, which demonstrate that VISA can achieve comparable approximation accuracy to standard importance-weighted forward-KL variational inference with computational savings of a factor two or more for conservatively chosen learning rates. | VISA: Variational Inference with Sequential Sample-Average Approximations | [
"Heiko Zimmermann",
"Christian A. Naesseth",
"Jan-Willem van de Meent"
] | NeurIPS.cc/2024/Conference | 2403.09429 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lZY9u0ijP7 | @inproceedings{
chen2024cascade,
title={Cascade Speculative Drafting for Even Faster {LLM} Inference},
author={Ziyi Chen and Xiaocong Yang and Jiacheng Lin and Chenkai Sun and Kevin Chang and Jie Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lZY9u0ijP7}
} | Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft. A larger target model then reviews this draft to align with its output, and any acceptance by the target model results in a reduction of the number of the target model runs, ultimately improving efficiency. However, the drafting process in speculative decoding includes slow autoregressive generation and allocates equal time to generating tokens, irrespective of their importance. These inefficiencies collectively contribute to the suboptimal performance of speculative decoding. To further improve LLM inference, we introduce Cascade Speculative Drafting (CS Drafting), a speculative execution algorithm that incorporates two types of cascades. The *Vertical Cascade* eliminates autoregressive generation from neural models, while the *Horizontal Cascade* optimizes time allocation in drafting for improved efficiency. Combining both cascades, CS Drafting achieves greater speedup compared to the baselines in our experiments, while preserving the same output distribution as the target model. Our code is publicly available at https://github.com/lfsszd/CS-Drafting. | Cascade Speculative Drafting for Even Faster LLM Inference | [
"Ziyi Chen",
"Xiaocong Yang",
"Jiacheng Lin",
"Chenkai Sun",
"Kevin Chang",
"Jie Huang"
] | NeurIPS.cc/2024/Conference | 2312.11462 | [
"https://github.com/lfsszd/cs-drafting"
] | https://huggingface.co/papers/2312.11462 | 3 | 8 | 1 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=lZJ0WYI5YC | @inproceedings{
jena2024deep,
title={Deep Learning in Medical Image Registration: Magic or Mirage?},
author={Rohit Jena and Deeksha Sethi and Pratik Chaudhari and James Gee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lZJ0WYI5YC}
} | Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. We reassess and recalibrate performance expectations from classical and DLIR methods under access to label supervision, training time, and its generalization capabilities under minor domain shifts. | Deep Learning in Medical Image Registration: Magic or Mirage? | [
"Rohit Jena",
"Deeksha Sethi",
"Pratik Chaudhari",
"James Gee"
] | NeurIPS.cc/2024/Conference | 2408.05839 | [
"https://github.com/rohitrango/Magic-or-Mirage"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=lYdjzx3DYu | @inproceedings{
huang2024emrmerging,
title={{EMR}-Merging: Tuning-Free High-Performance Model Merging},
author={Chenyu Huang and Peng Ye and Tao Chen and Tong He and Xiangyu Yue and Wanli Ouyang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lYdjzx3DYu}
} | The success of pretrain-finetune paradigm brings about the release of numerous model weights. In this case, merging models finetuned on different tasks to enable a single model with multi-task capabilities is gaining increasing attention for its practicability. Existing model merging methods usually suffer from (1) significant performance degradation or (2) requiring tuning by additional data or training. In this paper, we rethink and analyze the existing model merging paradigm. We discover that using a single model's weights can hardly simulate all the models' performance. To tackle this issue, we propose Elect, Mask & Rescale-Merging (EMR-Merging). We first (a) elect a unified model from all the model weights and then (b) generate extremely lightweight task-specific modulators, including masks and rescalers, to align the direction and magnitude between the unified model and each specific model, respectively. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance. We find that EMR-Merging shows outstanding performance compared to existing merging methods under different classical and newly-established settings, including merging different numbers of vision models (up to 30), NLP models, PEFT models, and multi-modal models. | EMR-Merging: Tuning-Free High-Performance Model Merging | [
"Chenyu Huang",
"Peng Ye",
"Tao Chen",
"Tong He",
"Xiangyu Yue",
"Wanli Ouyang"
] | NeurIPS.cc/2024/Conference | 2405.17461 | [
"https://github.com/harveyhuang18/emr_merging"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=lYPAYmfQqm | @inproceedings{
li2024finegrained,
title={Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond},
author={Yingcong Li and Ankit Singh Rawat and Samet Oymak},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lYPAYmfQqm}
} | Recent research has shown that Transformers with linear attention are capable of in-context learning (ICL) by implementing a linear estimator through gradient descent steps. However, the existing results on the optimization landscape apply under stylized settings where task and feature vectors are assumed to be IID and the attention weights are fully parameterized. In this work, we develop a stronger characterization of the optimization and generalization landscape of ICL through contributions on architectures, low-rank parameterization, and correlated designs: (1) We study the landscape of 1-layer linear attention and 1-layer H3, a state-space model. Under a suitable correlated design assumption, we prove that both implement 1-step preconditioned gradient descent. We show that thanks to its native convolution filters, H3 also has the advantage of implementing sample weighting and outperforming linear attention in suitable settings. (2) By studying correlated designs, we provide new risk bounds for retrieval augmented generation (RAG) and task-feature alignment which reveal how ICL sample complexity benefits from distributional alignment. (3) We derive the optimal risk for low-rank parameterized attention weights in terms of covariance spectrum. Through this, we also shed light on how LoRA can adapt to a new distribution by capturing the shift between task covariances. Experimental results corroborate our theoretical findings. Overall, this work explores the optimization and risk landscape of ICL in practically meaningful settings and contributes to a more thorough understanding of its mechanics. | Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond | [
"Yingcong Li",
"Ankit Singh Rawat",
"Samet Oymak"
] | NeurIPS.cc/2024/Conference | 2407.10005 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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