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null | https://openreview.net/forum?id=4t3ox9hj3z | @inproceedings{
park2024when,
title={When are dynamical systems learned from time series data statistically accurate?},
author={Jeongjin Park and Nicole Tianjiao Yang and Nisha Chandramoorthy},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4t3ox9hj3z}
} | Conventional notions of generalization often fail to describe the ability of learned models to capture meaningful information from dynamical data. A neural network that learns complex dynamics with a small test error may still fail to reproduce its \emph{physical} behavior, including associated statistical moments and Lyapunov exponents. To address this gap, we propose an ergodic theoretic approach to generalization of complex dynamical models learned from time series data. Our main contribution is to define and analyze generalization of a broad suite of neural representations of classes of ergodic systems, including chaotic systems, in a way that captures emulating underlying invariant, physical measures. Our results provide theoretical justification for why regression methods for generators of dynamical systems (Neural ODEs) fail to generalize, and why their statistical accuracy improves upon adding Jacobian information during training. We verify our results on a number of ergodic chaotic systems and neural network parameterizations, including MLPs, ResNets, Fourier Neural layers, and RNNs. | When are dynamical systems learned from time series data statistically accurate? | [
"Jeongjin Park",
"Nicole Tianjiao Yang",
"Nisha Chandramoorthy"
] | NeurIPS.cc/2024/Conference | 2411.06311 | [
"https://github.com/ni-sha-c/stacnode"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4syq5cgwA2 | @inproceedings{
pynadath2024gradientbased,
title={Gradient-based Discrete Sampling with Automatic Cyclical Scheduling},
author={Patrick Pynadath and Riddhiman Bhattacharya and ARUN NARAYANAN HARIHARAN and Ruqi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4syq5cgwA2}
} | Discrete distributions, particularly in high-dimensional deep models, are often highly multimodal due to inherent discontinuities. While gradient-based discrete sampling has proven effective, it is susceptible to becoming trapped in local modes due to the gradient information. To tackle this challenge, we propose an automatic cyclical scheduling, designed for efficient and accurate sampling in multimodal discrete distributions. Our method contains three key components: (1) a cyclical step size schedule where large steps discover new modes and small steps exploit each mode; (2) a cyclical balancing schedule, ensuring "balanced" proposals for given step sizes and high efficiency of the Markov chain; and (3) an automatic tuning scheme for adjusting the hyperparameters in the cyclical schedules, allowing adaptability across diverse datasets with minimal tuning. We prove the non-asymptotic convergence and inference guarantee for our method in general discrete distributions. Extensive experiments demonstrate the superiority of our method in sampling complex multimodal discrete distributions. | Gradient-based Discrete Sampling with Automatic Cyclical Scheduling | [
"Patrick Pynadath",
"Riddhiman Bhattacharya",
"ARUN NARAYANAN HARIHARAN",
"Ruqi Zhang"
] | NeurIPS.cc/2024/Conference | 2402.17699 | [
"https://github.com/patrickpynadath1/automatic_cyclical_sampling"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4sueqIwb4o | @inproceedings{
lim2024regularized,
title={Regularized Q-Learning},
author={Han-Dong Lim and Donghwan Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4sueqIwb4o}
} | Q-learning is widely used algorithm in reinforcement learning (RL) community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be unstable with the linear function approximation case. This paper develops a new Q-learning algorithm, called RegQ, that converges when linear function approximation is used. We prove that simply adding an appropriate regularization term ensures convergence of the algorithm. Its stability is established using a recent analysis tool based on switching system models. Moreover, we experimentally show that RegQ converges in environments where Q-learning with linear function approximation has known to diverge. An error bound on the solution where the algorithm converges is also given. | Regularized Q-Learning | [
"Han-Dong Lim",
"Donghwan Lee"
] | NeurIPS.cc/2024/Conference | 2202.05404 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4s5UsBUsUS | @inproceedings{
zhang2024vfimamba,
title={{VFIM}amba: Video Frame Interpolation with State Space Models},
author={Guozhen Zhang and Chunxu Liu and Yutao Cui and Xiaotong Zhao and Kai Ma and Limin Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4s5UsBUsUS}
} | Inter-frame modeling is pivotal in generating intermediate frames for video frame interpolation (VFI). Current approaches predominantly rely on convolution or attention-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, Selective State Space Models (S6) have emerged, tailored specifically for long sequence modeling, offering both linear complexity and data-dependent modeling capabilities. In this paper, we propose VFIMamba, a novel frame interpolation method for efficient and dynamic inter-frame modeling by harnessing the S6 model. Our approach introduces the Mixed-SSM Block (MSB), which initially rearranges tokens from adjacent frames in an interleaved fashion and subsequently applies multi-directional S6 modeling. This design facilitates the efficient transmission of information across frames while upholding linear complexity. Furthermore, we introduce a novel curriculum learning strategy that progressively cultivates proficiency in modeling inter-frame dynamics across varying motion magnitudes, fully unleashing the potential of the S6 model. Experimental findings showcase that our method attains state-of-the-art performance across diverse benchmarks, particularly excelling in high-resolution scenarios. In particular, on the X-TEST dataset, VFIMamba demonstrates a noteworthy improvement of 0.80 dB for 4K frames and 0.96 dB for 2K frames. | VFIMamba: Video Frame Interpolation with State Space Models | [
"Guozhen Zhang",
"Chunxu Liu",
"Yutao Cui",
"Xiaotong Zhao",
"Kai Ma",
"Limin Wang"
] | NeurIPS.cc/2024/Conference | 2407.02315 | [
"https://github.com/mcg-nju/vfimamba"
] | https://huggingface.co/papers/2407.02315 | 0 | 0 | 0 | 6 | [
"MCG-NJU/VFIMamba_S",
"MCG-NJU/VFIMamba",
"MCG-NJU/VFIMamba_ckpts"
] | [] | [] | [
"MCG-NJU/VFIMamba_S",
"MCG-NJU/VFIMamba",
"MCG-NJU/VFIMamba_ckpts"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4rrNcsVPDm | @inproceedings{
chen2024fnp,
title={{FNP}: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation},
author={Kun Chen and Peng Ye and Hao Chen and kang chen and Tao Han and Wanli Ouyang and Tao Chen and LEI BAI},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4rrNcsVPDm}
} | Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the Fourier Neural Processes (FNP) for arbitrary-resolution data assimilation in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks. Code is available at https://github.com/OpenEarthLab/FNP. | FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation | [
"Kun Chen",
"Peng Ye",
"Hao Chen",
"kang chen",
"Tao Han",
"Wanli Ouyang",
"Tao Chen",
"LEI BAI"
] | NeurIPS.cc/2024/Conference | 2406.01645 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4rCZeCZAON | @inproceedings{
guo2024do,
title={Do Finetti: On Causal Effects for Exchangeable Data},
author={Siyuan Guo and Chi Zhang and Karthika Mohan and Ferenc Husz{\'a}r and Bernhard Sch{\"o}lkopf},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4rCZeCZAON}
} | We study causal effect estimation in a setting where the data are not i.i.d.$\ $(independent and identically distributed). We focus on exchangeable data satisfying an assumption of independent causal mechanisms. Traditional causal effect estimation frameworks, e.g., relying on structural causal models and do-calculus, are typically limited to i.i.d. data and do not extend to more general exchangeable generative processes, which naturally arise in multi-environment data. To address this gap, we develop a generalized framework for exchangeable data and introduce a truncated factorization formula that facilitates both the identification and estimation of causal effects in our setting. To illustrate potential applications, we introduce a causal Pólya urn model and demonstrate how intervention propagates effects in exchangeable data settings. Finally, we develop an algorithm that performs simultaneous causal discovery and effect estimation given multi-environment data. | Do Finetti: On Causal Effects for Exchangeable Data | [
"Siyuan Guo",
"Chi Zhang",
"Karthika Mohan",
"Ferenc Huszár",
"Bernhard Schölkopf"
] | NeurIPS.cc/2024/Conference | 2405.18836 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=4php6bGL2W | @inproceedings{
huang2024seek,
title={Seek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual {RL}},
author={Yangru Huang and Peixi Peng and Yifan Zhao and Guangyao Chen and Yonghong Tian},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4php6bGL2W}
} | Accurate environment dynamics modeling is crucial for obtaining effective state representations in visual reinforcement learning (RL) applications. However, when facing multiple input modalities, existing dynamics modeling methods (e.g., DeepMDP) usually stumble in addressing the complex and volatile relationship between different modalities. In this paper, we study the problem of efficient dynamics modeling for multi-modal visual RL. We find that under the existence of modality heterogeneity, modality-correlated and distinct features are equally important but play different roles in reflecting the evolution of environmental dynamics. Motivated by this fact, we propose Dissected Dynamics Modeling (DDM), a novel multi-modal dynamics modeling method for visual RL. Unlike existing methods, DDM explicitly distinguishes consistent and inconsistent information across modalities and treats them separately with a divide-and-conquer strategy. This is done by dispatching the features carrying different information into distinct dynamics modeling pathways, which naturally form a series of implicit regularizations along the learning trajectories. In addition, a reward predictive function is further introduced to filter task-irrelevant information in both modality-consistent and inconsistent features, ensuring information integrity while avoiding potential distractions. Extensive experiments show that DDM consistently achieves competitive performance in challenging multi-modal visual environments. | Seek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual RL | [
"Yangru Huang",
"Peixi Peng",
"Yifan Zhao",
"Guangyao Chen",
"Yonghong Tian"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4pIfc51fGK | @inproceedings{
liu2024alleviate,
title={Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering},
author={Suyuan Liu and Siwei Wang and KE LIANG and Junpu Zhang and Zhibin Dong and Tianrui Liu and En Zhu and Xinwang Liu and Kunlun He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4pIfc51fGK}
} | Incomplete multi-view clustering aims to learn complete correlations among samples by leveraging complementary information across multiple views for clustering. Anchor-based methods further establish sample-level similarities for representative anchor generation, effectively addressing scalability issues in large-scale scenarios. Despite efficiency improvements, existing methods overlook the misguidance in anchors learning induced by partial missing samples, i.e., the absence of samples results in shift of learned anchors, further leading to sub-optimal clustering performance. To conquer the challenges, our solution involves a cross-view reconstruction strategy that not only alleviate the anchor shift problem through a carefully designed cross-view learning process, but also reconstructs missing samples in a way that transcends the limitations imposed by convex combinations. By employing affine combinations, our method explores areas beyond the convex hull defined by anchors, thereby illuminating blind spots in the reconstruction of missing samples. Experimental results on four benchmark datasets and three large-scale datasets validate the effectiveness of our proposed method. | Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering | [
"Suyuan Liu",
"Siwei Wang",
"KE LIANG",
"Junpu Zhang",
"Zhibin Dong",
"Tianrui Liu",
"En Zhu",
"Xinwang Liu",
"Kunlun He"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4pCu9c8leX | @inproceedings{
hou2024keygrid,
title={Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features},
author={Chengkai Hou and Zhengrong Xue and Bingyang Zhou and Jinghan Ke and Lin Shao and Huazhe Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4pCu9c8leX}
} | Detecting 3D keypoints with semantic consistency is widely used in many scenarios such as pose estimation, shape registration and robotics. Currently, most unsupervised 3D keypoint detection methods focus on the rigid-body objects. However, when faced with deformable objects, the keypoints they identify do not preserve semantic consistency well. In this paper, we introduce an innovative unsupervised keypoint detector Key-Grid for both the rigid-body and deformable objects, which is an autoencoder framework. The encoder predicts keypoints and the decoder utilizes the generated keypoints to reconstruct the objects. Unlike previous work, we leverage the identified keypoint in formation to form a 3D grid feature heatmap called grid heatmap, which is used in the decoder section. Grid heatmap is a novel concept that represents the latent variables for grid points sampled uniformly in the 3D cubic space, where these variables are the shortest distance between the grid points and the “skeleton” connected by keypoint pairs. Meanwhile, we incorporate the information from each layer of the encoder into the decoder section. We conduct an extensive evaluation of Key-Grid on a list of benchmark datasets. Key-Grid achieves the state-of-the-art performance on the semantic consistency and position accuracy of keypoints. Moreover, we demonstrate the robustness of Key-Grid to noise and downsampling. In addition, we achieve SE-(3) invariance of keypoints though generalizing Key-Grid to a SE(3)-invariant backbone. | Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features | [
"Chengkai Hou",
"Zhengrong Xue",
"Bingyang Zhou",
"Jinghan Ke",
"Lin Shao",
"Huazhe Xu"
] | NeurIPS.cc/2024/Conference | 2410.02237 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4oAt5L4lYe | @inproceedings{
chen2024arkvale,
title={ArkVale: Efficient Generative {LLM} Inference with Recallable Key-Value Eviction},
author={Renze Chen and Zhuofeng Wang and Beiquan Cao and Tong Wu and Size Zheng and Xiuhong Li and Xuechao Wei and Shengen Yan and Meng Li and Yun Liang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4oAt5L4lYe}
} | Large Language Models (LLMs) are widely used in today's tasks of natural language processing.
To support applications like multi-turn chats, document understanding, and content generation, models with long context lengths are growing in importance.
However, managing long contexts brings substantial challenges due to the expansion of key-value cache (KV cache). Longer KV cache requires larger memory, limiting the batch-size thus decreasing throughput. Also, computing attention over long KV cache incurs more memory access, hurting the end-to-end latency.
Prior works find that it is sufficient to use only the recent and high-impact tokens for attention computation, allowing the eviction of less vital tokens to shrink cache size.
Nonetheless, we observe a dynamic shift in token importance across different decoding steps. Tokens initially evicted might regain importance after certain decoding steps.
To address this, we propose ArkVale, a page-based KV cache manager that can recognize and recall currently important tokens evicted before. We asynchronously copy the filled page into external memory (e.g., CPU memory) as backup and summarize it into a much smaller digest by constructing the bounding-volume of its keys. Before attention computation, we measure all pages' importance based on their digests, recall the important ones, evict the unimportant ones, and select the top-ranked pages for attention computation.
Experiment results show that ArkVale performs well on various long context tasks with negligible accuracy loss under 2k$\sim$4k cache budget and can improve decoding latency to $2.2\times$ and batching throughput to $4.6\times$ because it applies attention on only a small subset of pages and reduce per-sample memory usage of KV cache. | ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction | [
"Renze Chen",
"Zhuofeng Wang",
"Beiquan Cao",
"Tong Wu",
"Size Zheng",
"Xiuhong Li",
"Xuechao Wei",
"Shengen Yan",
"Meng Li",
"Yun Liang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4neqdBz8eG | @inproceedings{
tian2024rethinking,
title={Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models},
author={Junjiao Tian and Chengyue Huang and Zsolt Kira},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4neqdBz8eG}
} | Modern optimizers such as AdamW, equipped with momentum and adaptive learning rate, are designed to escape local minima and explore the vast parameter space. This exploration is beneficial for finding good loss basins when training from scratch. It is not necessarily ideal when resuming from a powerful foundation model because it can lead to large deviations from the pre-trained initialization and, consequently, worse robustness and generalization. At the same time, strong regularization on all parameters can lead to under-fitting. We hypothesize that selectively regularizing the parameter space is the key to fitting and retraining the pre-trained knowledge. This paper proposes a new weight decay technique, Selective Projection Decay (SPD), that selectively imposes a strong penalty on certain layers while allowing others to change freely. Intuitively, SPD expands and contracts the parameter search space for layers with consistent and inconsistent loss reduction, respectively. Experimentally, when equipped with SPD, Adam consistently provides better in-distribution generalization and out-of-distribution robustness performance on multiple popular vision and language benchmarks. | Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models | [
"Junjiao Tian",
"Chengyue Huang",
"Zsolt Kira"
] | NeurIPS.cc/2024/Conference | 2411.01713 | [
"https://github.com/gt-ripl/selective-projection-decay"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4mzGiMooXM | @inproceedings{
zhuang2024magnet,
title={Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function},
author={Chenyi Zhuang and Ying Hu and Pan Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4mzGiMooXM}
} | Text-to-image diffusion models particularly Stable Diffusion, have revolutionized the field of computer vision. However, the synthesis quality often deteriorates when asked to generate images that faithfully represent complex prompts involving multiple attributes and objects. While previous studies suggest that blended text embeddings lead to improper attribute binding, few have explored this in depth. In this work, we critically examine the limitations of the CLIP text encoder in understanding attributes and investigate how this affects diffusion models. We discern a phenomenon of attribute bias in the text space and highlight a contextual issue in padding embeddings that entangle different concepts. We propose Magnet, a novel training-free approach to tackle the attribute binding problem. We introduce positive and negative binding vectors to enhance disentanglement, further with a neighbor strategy to increase accuracy. Extensive experiments show that Magnet significantly improves synthesis quality and binding accuracy with negligible computational cost, enabling the generation of unconventional and unnatural concepts. | Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function | [
"Chenyi Zhuang",
"Ying Hu",
"Pan Gao"
] | NeurIPS.cc/2024/Conference | 2409.19967 | [
"https://github.com/i2-multimedia-lab/magnet"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4mxzxYhMuN | @inproceedings{
song2024motion,
title={Motion Forecasting in Continuous Driving},
author={Nan Song and Bozhou Zhang and Xiatian Zhu and Li Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4mxzxYhMuN}
} | Motion forecasting for agents in autonomous driving is highly challenging due to the numerous possibilities for each agent's next action and their complex interactions in space and time.
In real applications, motion forecasting takes place repeatedly and continuously as the self-driving car moves. However, existing forecasting methods typically process each driving scene within a certain range independently, totally ignoring the situational and contextual relationships between successive driving scenes. This significantly simplifies the forecasting task, making the solutions suboptimal and inefficient to use in practice. To address this fundamental limitation, we propose a novel motion forecasting framework for continuous driving, named RealMotion.
It comprises two integral streams both at the scene level:
(1) The scene context stream progressively accumulates historical scene information until the present moment, capturing temporal interactive relationships among scene elements.
(2) The agent trajectory stream optimizes current forecasting by sequentially relaying past predictions.
Besides, a data reorganization strategy is introduced to narrow the gap between existing benchmarks and real-world applications, consistent with our network. These approaches enable exploiting more broadly the situational and progressive insights of dynamic motion across space and time.
Extensive experiments on Argoverse series with different settings demonstrate that our RealMotion achieves state-of-the-art performance, along with the advantage of efficient real-world inference. | Motion Forecasting in Continuous Driving | [
"Nan Song",
"Bozhou Zhang",
"Xiatian Zhu",
"Li Zhang"
] | NeurIPS.cc/2024/Conference | 2410.06007 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=4lGPSbGe11 | @inproceedings{
iyengar2024is,
title={Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?},
author={Garud Iyengar and Henry Lam and Tianyu Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4lGPSbGe11}
} | Cross-Validation (CV) is the default choice for estimate the out-of-sample performance of machine learning models. Despite its wide usage, their statistical benefits have remained half-understood, especially in challenging nonparametric regimes. In this paper we fill in this gap and show that, in terms of estimating the out-of-sample performances, for a wide spectrum of models, CV does not statistically outperform the simple ``plug-in'' approach where one reuses training data for testing evaluation. Specifically, in terms of both the asymptotic bias and coverage accuracy of the associated interval for out-of-sample evaluation, $K$-fold CV provably cannot outperform plug-in regardless of the rate at which the parametric or nonparametric models converge. Leave-one-out CV can have a smaller bias as compared to plug-in; however, this bias improvement is negligible compared to the variability of the evaluation, and in some important cases leave-one-out again does not outperform plug-in once this variability is taken into account. We obtain our theoretical comparisons via a novel higher-order Taylor analysis that dissects the limit theorems of testing evaluations, which applies to model classes that are not amenable to previously known sufficient conditions. Our numerical results demonstrate that plug-in performs indeed no worse than CV in estimating model performance across a wide range of examples. | Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? | [
"Garud Iyengar",
"Henry Lam",
"Tianyu Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4kVHI2uXRE | @inproceedings{
wu2024goal,
title={Goal Conditioned Reinforcement Learning for Photo Finishing Tuning},
author={Jiarui Wu and Yujin Wang and Lingen Li and Zhang Fan and Tianfan Xue},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4kVHI2uXRE}
} | Photo finishing tuning aims to automate the manual tuning process of the photo finishing pipeline, like Adobe Lightroom or Darktable. Previous works either use zeroth-order optimization, which is slow when the set of parameters increases, or rely on a differentiable proxy of the target finishing pipeline, which is hard to train.
To overcome these challenges, we propose a novel goal-conditioned reinforcement learning framework for efficiently tuning parameters using a goal image as a condition. Unlike previous approaches, our tuning framework does not rely on any proxy and treats the photo finishing pipeline as a black box. Utilizing a trained reinforcement learning policy, it can efficiently find the desired set of parameters within just 10 queries, while optimization based approaches normally take 200 queries. Furthermore, our architecture utilizes a goal image to guide the iterative tuning of pipeline parameters, allowing for flexible conditioning on pixel-aligned target images, style images, or any other visually representable goals. We conduct detailed experiments on photo finishing tuning and photo stylization tuning tasks, demonstrating the advantages of our method. | Goal Conditioned Reinforcement Learning for Photo Finishing Tuning | [
"Jiarui Wu",
"Yujin Wang",
"Lingen Li",
"Zhang Fan",
"Tianfan Xue"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4jn7KWPHSD | @inproceedings{
miao2024improving,
title={Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective},
author={Yibo Miao and Yinpeng Dong and Jinlai Zhang and Lijia Yu and Xiao Yang and Xiao-Shan Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4jn7KWPHSD}
} | Although 3D point cloud recognition has achieved substantial progress on standard benchmarks, the typical models are vulnerable to point cloud corruptions, leading to security threats in real-world applications. To improve the corruption robustness, various data augmentation methods have been studied, but they are mainly limited to the spatial domain. As the point cloud has low information density and significant spatial redundancy, it is challenging to analyze the effects of corruptions. In this paper, we focus on the frequency domain to observe the underlying structure of point clouds and their corruptions. Through graph Fourier transform (GFT), we observe a correlation between the corruption robustness of point cloud recognition models and their sensitivity to different frequency bands, which is measured by the GFT spectrum of the model’s Jacobian matrix. To reduce the sensitivity and improve the corruption robustness, we propose Frequency Adversarial Training (FAT) that adopts frequency-domain adversarial examples as data augmentation to train robust point cloud recognition models against corruptions. Theoretically, we provide a guarantee of FAT on its out-of-distribution generalization performance. Empirically, we conduct extensive experiments with various network architectures to validate the effectiveness of FAT, which achieves the new state-of-the-art results. | Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective | [
"Yibo Miao",
"Yinpeng Dong",
"Jinlai Zhang",
"Lijia Yu",
"Xiao Yang",
"Xiao-Shan Gao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4jegYnUMHb | @inproceedings{
gao2024metauas,
title={Meta{UAS}: Universal Anomaly Segmentation with One-Prompt Meta-Learning},
author={Bin-Bin Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4jegYnUMHb}
} | Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual representations are inherently independent of language. In this paper, we explore the potential of a pure visual foundation model as an alternative to widely used vision-language models for universal visual anomaly segmentation.
We present a novel paradigm that unifies anomaly segmentation into change segmentation. This paradigm enables us to leverage large-scale synthetic image pairs, featuring object-level and local region changes, derived from existing image datasets, which are independent of target anomaly datasets. We propose a one-prompt Meta-learning framework for Universal Anomaly Segmentation (MetaUAS) that is trained on this synthetic dataset and then generalizes well to segment any novel or unseen visual anomalies in the real world. To handle geometrical variations between prompt and query images, we propose a soft feature alignment module that bridges paired-image change perception and single-image semantic segmentation. This is the first work to achieve universal anomaly segmentation using a pure vision model without relying on special anomaly detection datasets and pre-trained visual-language models. Our method effectively and efficiently segments any anomalies with only one normal image prompt and enjoys training-free without guidance from language. Our MetaUAS significantly outperforms previous zero-shot, few-shot, and even full-shot anomaly segmentation methods. Code and Models: https://github.com/gaobb/MetaUAS. | MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning | [
"Bin-Bin Gao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4jXaca2NYa | @inproceedings{
ma2024zopp,
title={{ZOPP}: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving},
author={Tao MA and Hongbin Zhou and Qiusheng Huang and Xuemeng Yang and Jianfei Guo and Bo Zhang and Min Dou and Yu Qiao and Botian Shi and Hongsheng Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4jXaca2NYa}
} | Offboard perception aims to automatically generate high-quality 3D labels for autonomous driving (AD) scenes. Existing offboard methods focus on 3D object detection with closed-set taxonomy and fail to match human-level recognition capability on the rapidly evolving perception tasks. Due to heavy reliance on human labels and the prevalence of data imbalance and sparsity, a unified framework for offboard auto-labeling various elements in AD scenes that meets the distinct needs of perception tasks is not being fully explored. In this paper, we propose a novel multi-modal Zero-shot Offboard Panoptic Perception (ZOPP) framework for autonomous driving scenes. ZOPP integrates the powerful zero-shot recognition capabilities of vision foundation models and 3D representations derived from point clouds. To the best of our knowledge, ZOPP represents a pioneering effort in the domain of multi-modal panoptic perception and auto labeling for autonomous driving scenes. We conduct comprehensive empirical studies and evaluations on Waymo open dataset to validate the proposed ZOPP on various perception tasks. To further explore the usability and extensibility of our proposed ZOPP, we also conduct experiments in downstream applications. The results further demonstrate the great potential of our ZOPP for real-world scenarios. Code will be released at \url{https://github.com/PJLab-ADG/ZOPP}. | ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving | [
"Tao MA",
"Hongbin Zhou",
"Qiusheng Huang",
"Xuemeng Yang",
"Jianfei Guo",
"Bo Zhang",
"Min Dou",
"Yu Qiao",
"Botian Shi",
"Hongsheng Li"
] | NeurIPS.cc/2024/Conference | 2411.05311 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4jRNkAH15k | @inproceedings{
zhou2024detail,
title={{DETAIL}: Task {DE}monsTration Attribution for Interpretable In-context Learning},
author={Zijian Zhou and Xiaoqiang Lin and Xinyi Xu and Alok Prakash and Daniela Rus and Bryan Kian Hsiang Low},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4jRNkAH15k}
} | In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without updating their parameters, significantly boosting their flexibility and generality. ICL possesses many distinct characteristics from conventional machine learning, thereby requiring new approaches to interpret this learning paradigm. Taking the viewpoint of recent works showing that transformers learn in context by formulating an internal optimizer, we propose an influence function-based attribution technique, DETAIL, that addresses the specific characteristics of ICL. We empirically verify the effectiveness of our approach for demonstration attribution while being computationally efficient. Leveraging the results, we then show how DETAIL can help improve model performance in real-world scenarios through demonstration reordering and curation. Finally, we experimentally prove the wide applicability of DETAIL by showing our attribution scores obtained on white-box models are transferable to black-box models in improving model performance. | DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning | [
"Zijian Zhou",
"Xiaoqiang Lin",
"Xinyi Xu",
"Alok Prakash",
"Daniela Rus",
"Bryan Kian Hsiang Low"
] | NeurIPS.cc/2024/Conference | 2405.14899 | [
"https://github.com/BobbyZhouZijian/detail_release"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4i9xuPEu9w | @inproceedings{
lin2024because,
title={{BECAUSE}: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning},
author={Haohong Lin and Wenhao Ding and Jian Chen and Laixi Shi and Jiacheng Zhu and Bo Li and Ding Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4i9xuPEu9w}
} | Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies, especially in scenarios where exploration is costly or infeasible. Nevertheless, its performance often suffers from the objective mismatch between model and policy learning, resulting in inferior performance despite accurate model predictions. This paper first identifies the primary source of this mismatch comes from the underlying confounders present in offline data for MBRL. Subsequently, we introduce **B**ilin**E**ar **CAUS**al r**E**presentation (BECAUSE), an algorithm to capture causal representation for both states and actions to reduce the influence of the distribution shift, thus mitigating the objective mismatch problem. Comprehensive evaluations on 18 tasks that vary in data quality and environment context demonstrate the superior performance of BECAUSE over existing offline RL algorithms. We show the generalizability and robustness of BECAUSE under fewer samples or larger numbers of confounders. Additionally, we offer theoretical analysis of BECAUSE to prove its error bound and sample efficiency when integrating causal representation into offline MBRL. See more details in our project page: [https://sites.google.com/view/be-cause](https://sites.google.com/view/be-cause). | BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning | [
"Haohong Lin",
"Wenhao Ding",
"Jian Chen",
"Laixi Shi",
"Jiacheng Zhu",
"Bo Li",
"Ding Zhao"
] | NeurIPS.cc/2024/Conference | 2407.10967 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4fSSqpk1sM | @inproceedings{
porian2024resolving,
title={Resolving Discrepancies in Compute-Optimal Scaling of Language Models},
author={Tomer Porian and Mitchell Wortsman and Jenia Jitsev and Ludwig Schmidt and Yair Carmon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4fSSqpk1sM}
} | Kaplan et al. and Hoffmann et al. developed influential scaling laws for the optimal model size as a function of the compute budget, but these laws yield substantially different predictions. We explain the discrepancy by reproducing the Kaplan scaling law on two datasets (OpenWebText2 and RefinedWeb) and identifying three factors causing the difference: last layer computational cost, warmup duration, and scale-dependent optimizer tuning. With these factors corrected, we obtain excellent agreement with the Hoffmann et al. (i.e., "Chinchilla") scaling law. Counter to a hypothesis of Hoffmann et al., we find that careful learning rate decay is not essential for the validity of their scaling law. As a secondary result, we derive scaling laws for the optimal learning rate and batch size, finding that tuning the AdamW $\beta_2$ parameter is essential at lower batch sizes. | Resolving Discrepancies in Compute-Optimal Scaling of Language Models | [
"Tomer Porian",
"Mitchell Wortsman",
"Jenia Jitsev",
"Ludwig Schmidt",
"Yair Carmon"
] | NeurIPS.cc/2024/Conference | 2406.19146 | [
"https://github.com/formll/resolving-scaling-law-discrepencies"
] | https://huggingface.co/papers/2406.19146 | 2 | 0 | 0 | 5 | [
"formll/resolving-scaling-law-discrepancies"
] | [] | [] | [
"formll/resolving-scaling-law-discrepancies"
] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=4fN2REs0Ma | @inproceedings{
chen2024unveiling,
title={Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers},
author={Siyu Chen and Heejune Sheen and Tianhao Wang and Zhuoran Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4fN2REs0Ma}
} | In-context learning (ICL) is a cornerstone of large language model (LLM) functionality, yet its theoretical foundations remain elusive due to the complexity of transformer architectures. In particular, most existing work only theoretically explains how the attention mechanism facilitates ICL under certain data models. It remains unclear how the other building blocks of the transformer contribute to ICL. To address this question, we study how a two-attention-layer transformer is trained to perform ICL on $n$-gram Markov chain data, where each token in the Markov chain statistically depends on the previous n tokens.
We analyze a sophisticated transformer model featuring relative positional embedding, multi-head softmax attention, and a feed-forward layer with normalization.
We prove that the gradient flow with respect to a cross-entropy ICL loss converges to a limiting model that performs a generalized version of the "induction head" mechanism with a learned feature, resulting from the congruous contribution of all the building blocks.
Specifically, the first attention layer acts as a copier, copying past tokens within a given window to each position, and the feed-forward network with normalization acts as a selector that generates a feature vector by only looking at informationally relevant parents from the window.
Finally, the second attention layer is a classifier that
compares these features with the feature at the output position, and uses the resulting similarity scores to generate the desired output. Our theory is further validated by simulation experiments. | Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers | [
"Siyu Chen",
"Heejune Sheen",
"Tianhao Wang",
"Zhuoran Yang"
] | NeurIPS.cc/2024/Conference | 2409.10559 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4czwwExZKQ | @inproceedings{
jiang2024actsort,
title={ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets},
author={Yiqi Jiang and Hakki Orhun Akengin and Ji Zhou and Mehmet Anil Aslihak and Yang Li and Radoslaw Chrapkiewicz and Oscar Hernandez and Sadegh Ebrahimi and Omar Jaidar and Yanping Zhang and Hakan Inan and Christopher Miranda and Fatih Dinc and Marta Blanco-Pozo and Mark Schnitzer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4czwwExZKQ}
} | Recent advances in calcium imaging enable simultaneous recordings of up to a million neurons in behaving animals, producing datasets of unprecedented scales. Although individual neurons and their activity traces can be extracted from these videos with automated algorithms, the results often require human curation to remove false positives, a laborious process called \emph{cell sorting}. To address this challenge, we introduce ActSort, an active-learning algorithm for sorting large-scale datasets that integrates features engineered by domain experts together with data formats with minimal memory requirements. By strategically bringing outlier cell candidates near the decision boundary up for annotation, ActSort reduces human labor to about 1–3\% of cell candidates and improves curation accuracy by mitigating annotator bias. To facilitate the algorithm's widespread adoption among experimental neuroscientists, we created a user-friendly software and conducted a first-of-its-kind benchmarking study involving about 160,000 annotations. Our tests validated ActSort's performance across different experimental conditions and datasets from multiple animals. Overall, ActSort addresses a crucial bottleneck in processing large-scale calcium videos of neural activity and thereby facilitates systems neuroscience experiments at previously inaccessible scales. (\url{https://github.com/schnitzer-lab/ActSort-public}) | ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets | [
"Yiqi Jiang",
"Hakki Orhun Akengin",
"Ji Zhou",
"Mehmet Anil Aslihak",
"Yang Li",
"Radoslaw Chrapkiewicz",
"Oscar Hernandez",
"Sadegh Ebrahimi",
"Omar Jaidar",
"Yanping Zhang",
"Hakan Inan",
"Christopher Miranda",
"Fatih Dinc",
"Marta Blanco-Pozo",
"Mark Schnitzer"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4cU9ZvOkBz | @inproceedings{
hothem2024what,
title={What is my quantum computer good for? Quantum capability learning with physics-aware neural networks},
author={Daniel Hothem and Ashe Miller and Timothy Proctor},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4cU9ZvOkBz}
} | Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers experience errors that often cause quantum programs run on them to fail. Until quantum computers can reliably execute large quantum programs, stakeholders will need fast and reliable methods for assessing a quantum computer’s capability—i.e., the programs it can run and how well it can run them. Previously, off-the-shelf neural network architectures have been used to model quantum computers' capabilities, but with limited success, because these networks fail to learn the complex quantum physics that determines real quantum computers' errors. We address this shortcoming with a new quantum-physics-aware neural network architecture for learning capability models. Our scalable architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $\sim50\%$ reductions in mean absolute error on both experimental and simulated data, over state-of-the-art models based on convolutional neural networks, and scales to devices with 100+ qubits. | What is my quantum computer good for? Quantum capability learning with physics-aware neural networks | [
"Daniel Hothem",
"Ashe Miller",
"Timothy Proctor"
] | NeurIPS.cc/2024/Conference | 2406.05636 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4bKEFyUHT4 | @inproceedings{
petersen2024convolutional,
title={Convolutional Differentiable Logic Gate Networks},
author={Felix Petersen and Hilde Kuehne and Christian Borgelt and Julian Welzel and Stefano Ermon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4bKEFyUHT4}
} | With the increasing inference cost of machine learning models, there is a growing interest in models with fast and efficient inference.
Recently, an approach for learning logic gate networks directly via a differentiable relaxation was proposed. Logic gate networks are faster than conventional neural network approaches because their inference only requires logic gate operators such as NAND, OR, and XOR, which are the underlying building blocks of current hardware and can be efficiently executed. We build on this idea, extending it by deep logic gate tree convolutions, logical OR pooling, and residual initializations. This allows scaling logic gate networks up by over one order of magnitude and utilizing the paradigm of convolution. On CIFAR-10, we achieve an accuracy of 86.29% using only 61 million logic gates, which improves over the SOTA while being 29x smaller. | Convolutional Differentiable Logic Gate Networks | [
"Felix Petersen",
"Hilde Kuehne",
"Christian Borgelt",
"Julian Welzel",
"Stefano Ermon"
] | NeurIPS.cc/2024/Conference | 2411.04732 | [
"https://github.com/felix-petersen/difflogic"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=4bJufOS6No | @inproceedings{
song2024on,
title={On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection},
author={Xiufeng Song and Xiao Guo and Jiache Zhang and Qirui Li and LEI BAI and Xiaoming Liu and Guangtao Zhai and Xiaohong Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4bJufOS6No}
} | Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection. However, existing video-level detection algorithms primarily focus on detecting facial forgeries and often fail to identify diffusion-generated content with a diverse range of semantics. To advance the field of video forensics, we propose an innovative algorithm named Multi-Modal Detection(MM-Det) for detecting diffusion-generated videos. MM-Det utilizes the profound perceptual and comprehensive abilities of Large Multi-modal Models (LMMs) by generating a Multi-Modal Forgery Representation (MMFR) from LMM's multi-modal space, enhancing its ability to detect unseen forgery content. Besides, MM-Det leverages an In-and-Across Frame Attention (IAFA) mechanism for feature augmentation in the spatio-temporal domain. A dynamic fusion strategy helps refine forgery representations for the fusion. Moreover, we construct a comprehensive diffusion video dataset, called Diffusion Video Forensics (DVF), across a wide range of forgery videos. MM-Det achieves state-of-the-art performance in DVF, demonstrating the effectiveness of our algorithm. Both source code and DVF are available at https://github.com/SparkleXFantasy/MM-Det. | On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection | [
"Xiufeng Song",
"Xiao Guo",
"Jiache Zhang",
"Qirui Li",
"LEI BAI",
"Xiaoming Liu",
"Guangtao Zhai",
"Xiaohong Liu"
] | NeurIPS.cc/2024/Conference | 2410.23623 | [
"https://github.com/sparklexfantasy/mm-det"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4bINoegDcm | @inproceedings{
pang2024attndreambooth,
title={AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation},
author={Lianyu Pang and Jian Yin and Baoquan Zhao and Feize Wu and Fu Lee Wang and Qing Li and Xudong Mao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4bINoegDcm}
} | Recent advances in text-to-image models have enabled high-quality personalized image synthesis based on user-provided concepts with flexible textual control. In this work, we analyze the limitations of two primary techniques in text-to-image personalization: Textual Inversion and DreamBooth. When integrating the learned concept into new prompts, Textual Inversion tends to overfit the concept, while DreamBooth often overlooks it. We attribute these issues to the incorrect learning of the embedding alignment for the concept. To address this, we introduce AttnDreamBooth, a novel approach that separately learns the embedding alignment, the attention map, and the subject identity across different training stages. We also introduce a cross-attention map regularization term to enhance the learning of the attention map. Our method demonstrates significant improvements in identity preservation and text alignment compared to the baseline methods. | AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation | [
"Lianyu Pang",
"Jian Yin",
"Baoquan Zhao",
"Feize Wu",
"Fu Lee Wang",
"Qing Li",
"Xudong Mao"
] | NeurIPS.cc/2024/Conference | 2406.05000 | [
""
] | https://huggingface.co/papers/2406.05000 | 1 | 1 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4aEwZkWB5z | @inproceedings{
diakonikolas2024a,
title={A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise},
author={Ilias Diakonikolas and Nikos Zarifis},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4aEwZkWB5z}
} | We study the problem of PAC learning $\gamma$-margin halfspaces in the presence of Massart noise.
Without computational considerations, the sample complexity of this learning problem is known to be
$\widetilde{\Theta}(1/(\gamma^2 \epsilon))$.
Prior computationally efficient algorithms for the problem incur sample complexity
$\tilde{O}(1/(\gamma^4 \epsilon^3))$ and achieve 0-1 error of $\eta+\epsilon$,
where $\eta<1/2$ is the upper bound on the noise rate.
Recent work gave evidence of an information-computation tradeoff,
suggesting that a quadratic dependence on $1/\epsilon$ is required
for computationally efficient algorithms.
Our main result is a computationally efficient learner with sample complexity
$\widetilde{\Theta}(1/(\gamma^2 \epsilon^2))$, nearly matching this lower bound.
In addition, our algorithm is simple and practical,
relying on online SGD on a carefully selected sequence of convex losses. | A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise | [
"Ilias Diakonikolas",
"Nikos Zarifis"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=4Zt7S0B0Jp | @inproceedings{
wang2024chainofthought,
title={Chain-of-Thought Reasoning Without Prompting},
author={Xuezhi Wang and Denny Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4Zt7S0B0Jp}
} | In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without any prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the \textit{decoding} process. Rather than conventional greedy decoding, we investigate the top-$k$ alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' \textit{intrinsic} reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding effectively elicits reasoning capabilities from language models, which were previously obscured by standard greedy decoding. | Chain-of-Thought Reasoning Without Prompting | [
"Xuezhi Wang",
"Denny Zhou"
] | NeurIPS.cc/2024/Conference | 2402.10200 | [
""
] | https://huggingface.co/papers/2402.10200 | 1 | 100 | 3 | 2 | [] | [] | [
"codelion/optillm",
"fabiodr/optillm"
] | [] | [] | [
"codelion/optillm",
"fabiodr/optillm"
] | 1 | poster |
null | https://openreview.net/forum?id=4ZH48aGD60 | @inproceedings{
xu2024active,
title={Active, anytime-valid risk controlling prediction sets},
author={Ziyu Xu and Nikos Karampatziakis and Paul Mineiro},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4ZH48aGD60}
} | Rigorously establishing the safety of black-box machine learning models with respect to critical risk measures is important for providing guarantees about the behavior of the model.
Recently, a notion of a risk controlling prediction set (RCPS) has been introduced by Bates et. al. (JACM '24) for producing prediction sets that are statistically guaranteed to have low risk from machine learning models.
Our method extends this notion to the sequential setting, where we provide guarantees even when the data is collected adaptively, and ensures the risk guarantee is anytime-valid, i.e., simultaneously holds at all time steps. Further, we propose a framework for constructing RCPSes for active labeling, i.e., allowing one to use a labeling policy that chooses whether to query the true label for each received data point, and ensures the expected proportion data points whose labels are queried are below a predetermined label budget. We also describe how to use predictors (e.g., the machine learning model we are providing risk control guarantees for) to further improve the utility of our RCPSes by estimating the expected risk conditioned on the covariates.
We characterize the optimal choices of label policy under a fixed label budget, and predictor, and show a regret result that relates the estimation error of the optimal labeling policy and predictor to the wealth process that underlies our RCPSes.
Lastly, we present practical ways of formulating label policies and we empirically show that our label policies use fewer labels to reach higher utility than naive baseline labeling strategies on both simulations and real data. | Active, anytime-valid risk controlling prediction sets | [
"Ziyu Xu",
"Nikos Karampatziakis",
"Paul Mineiro"
] | NeurIPS.cc/2024/Conference | 2406.10490 | [
"https://github.com/neilzxu/active-rcps"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4Z7RZixpJQ | @inproceedings{
li2024prosst,
title={Pro{SST}: Protein Language Modeling with Quantized Structure and Disentangled Attention},
author={Mingchen Li and Yang Tan and Xinzhu Ma and Bozitao Zhong and Huiqun Yu and Ziyi Zhou and Wanli Ouyang and Bingxin Zhou and Pan Tan and Liang Hong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4Z7RZixpJQ}
} | Protein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transformer-based protein language model that seamlessly integrates both protein sequences and structures. ProSST incorporates a structure quantization module and a Transformer architecture with disentangled attention. The structure quantization module translates a 3D protein structure into a sequence of discrete tokens by first serializing the protein structure into residue-level local structures and then embeds them into dense vector space. These vectors are then quantized into discrete structure tokens by a pre-trained clustering model. These tokens serve as an effective protein structure representation. Furthermore, ProSST explicitly learns the relationship between protein residue token sequences and structure token sequences through the sequence-structure disentangled attention. We pre-train ProSST on millions of protein structures using a masked language model objective, enabling it to learn comprehensive contextual representations of proteins. To evaluate the proposed ProSST, we conduct extensive experiments on the zero-shot mutation effect prediction and several supervised downstream tasks, where ProSST achieves the state-of-the-art performance among all baselines. Our code and pre-trained models are publicly available. | ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention | [
"Mingchen Li",
"Yang Tan",
"Xinzhu Ma",
"Bozitao Zhong",
"Huiqun Yu",
"Ziyi Zhou",
"Wanli Ouyang",
"Bingxin Zhou",
"Pan Tan",
"Liang Hong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4Yj7L9Kt7t | @inproceedings{
cheng2024taming,
title={Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting},
author={Duo Cheng and Xingyu Zhou and Bo Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4Yj7L9Kt7t}
} | In this paper, we study the multi-armed bandit problem in the best-of-both-worlds (BOBW) setting with heavy-tailed losses, where the losses can be negative and unbounded but have $(1+v)$-th raw moments bounded by $u^{1+v}$ for some known $u>0$ and $v\in(0,1]$. Specifically, we consider the BOBW setting where the underlying environment could be either (oblivious) adversarial (i.e., the loss distribution can change arbitrarily over time) or stochastic (i.e., the loss distribution is fixed over time) and is unknown to the decision-maker a prior, and propose an algorithm that achieves a $T^{\frac{1}{1+v}}$-type worst-case (pseudo-)regret in the adversarial regime and a $\log T$-type gap-dependent regret in the stochastic regime, where $T$ is the time horizon. Compared to the state-of-the-art results, our algorithm offers stronger \emph{high-probability} regret guarantees rather than expected regret guarantees, and more importantly, relaxes a strong technical assumption on the loss distribution. This assumption is needed even for the weaker expected regret obtained in the literature and is generally hard to verify in practice. As a byproduct, relaxing this assumption leads to the first near-optimal regret result for heavy-tailed bandits with Huber contamination in the adversarial regime, in contrast to all previous works focused on the (easier) stochastic regime. Our result also implies a high-probability BOBW regret guarantee when the bounded true losses are protected with pure Local Differential Privacy (LDP), while the existing work ensures the (weaker) \emph{approximate} LDP with the regret bounds in expectation only. | Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting | [
"Duo Cheng",
"Xingyu Zhou",
"Bo Ji"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4XTvXMSZPO | @inproceedings{
bai2024digirl,
title={Digi{RL}: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning},
author={Hao Bai and Yifei Zhou and Jiayi Pan and Mert Cemri and Alane Suhr and Sergey Levine and Aviral Kumar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4XTvXMSZPO}
} | Pre-trained vision language models (VLMs), though powerful, typically lack training on decision-centric data, rendering them sub-optimal for decision-making tasks such as in-the-wild device control through Graphical User Interfaces (GUIs) when used off-the-shelf. While training with static demonstrations has shown some promise, we show that such methods fall short when controlling real GUIs due to their failure to deal with real world stochasticity and dynamism not captured in static observational data. This paper introduces a novel autonomous RL approach, called DigiRL, for training in-the-wild device control agents through fine-tuning a pre-trained VLM in two stages: offline and offline-to-online RL. We first build a scalable and parallelizable Android learning environment equipped with a VLM-based general-purpose evaluator and then identify the key design choices for simple and effective RL in this domain. We demonstrate the effectiveness of DigiRL using the Android-in-the-Wild (AitW) dataset, where our 1.5B VLM trained with RL achieves a 49.5\% absolute improvement -- from 17.7 to 67.2\% success rate -- over supervised fine-tuning with static human demonstration data. It is worth noting that such improvement is achieved without any additional supervision or demonstration data. These results significantly surpass not only the prior best agents, including AppAgent with GPT-4V (8.3\% success rate) and the 17B CogAgent trained with AitW data (14.4\%), but also our implementation of prior best autonomous RL approach based on filtered behavior cloning (57.8\%), thereby establishing a new state-of-the-art for digital agents for in-the-wild device control. | DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning | [
"Hao Bai",
"Yifei Zhou",
"Jiayi Pan",
"Mert Cemri",
"Alane Suhr",
"Sergey Levine",
"Aviral Kumar"
] | NeurIPS.cc/2024/Conference | 2406.11896 | [
""
] | https://huggingface.co/papers/2406.11896 | 3 | 18 | 1 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4XIKfvNYvx | @inproceedings{
pang2024iterative,
title={Iterative Reasoning Preference Optimization},
author={Richard Yuanzhe Pang and Weizhe Yuan and He He and Kyunghyun Cho and Sainbayar Sukhbaatar and Jason E Weston},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4XIKfvNYvx}
} | Iterative preference optimization methods have recently been shown to perform well for general instruction tuning tasks, but typically make little improvement on reasoning tasks. In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by optimizing for winning vs. losing reasoning steps. We train using a modified DPO loss with an additional negative log-likelihood term, which we find to be crucial. We show reasoning improves across repeated iterations of this scheme. While only relying on examples in the training set, our approach results in increasing accuracy on GSM8K, MATH, and ARC-Challenge for Llama-2-70B-Chat, outperforming other Llama-2-based models not relying on additionally sourced datasets. For example, we see a large improvement from 55.6% to 81.6% on GSM8K and an accuracy of 88.7% with majority voting out of 32 samples. | Iterative Reasoning Preference Optimization | [
"Richard Yuanzhe Pang",
"Weizhe Yuan",
"He He",
"Kyunghyun Cho",
"Sainbayar Sukhbaatar",
"Jason E Weston"
] | NeurIPS.cc/2024/Conference | 2404.19733 | [
""
] | https://huggingface.co/papers/2404.19733 | 5 | 47 | 4 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4WIBvL6ZF4 | @inproceedings{
li2024dynamic,
title={Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments},
author={Yanping Li and Jingshen Wang and Waverly Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4WIBvL6ZF4}
} | Identifying subgroups with differential responses to treatment is pivotal in randomized clinical trials, as tailoring treatments to specific subgroups can advance personalized medicine. Upon trial completion, identifying best-performing subgroups–those with the most beneficial treatment effects–is crucial for optimizing resource allocation or mitigating adverse treatment effects. However, traditional clinical trials are not customized for the goal of identifying best-performing subgroups because they typically pre-define subgroups at the beginning of the trial and adhere to a fixed subgroup treatment allocation rule, leading to inefficient use of experimental efforts. While some adaptive experimental strategies exist for the identification of the single best subgroup, they commonly do not enable the identification of the best set of subgroups. To address these challenges, we propose a dynamic subgroup identification covariate-adjusted response-adaptive randomization (CARA) design strategy with the following key features: (i) Our approach is an adaptive experimental strategy that allows the dynamic identification of the best subgroups and the revision of treatment allocation towards the goal of correctly identifying the best subgroups based on collected experimental data. (ii) Our design handles ties between subgroups effectively, merging those with similar treatment effects to maximize experimental efficiency. In the theoretical investigations, we demonstrate that our design has a higher probability of correctly identifying the best set of subgroups compared to conventional designs. Additionally, we prove the statistical validity of our estimator for the best subgroup treatment effect, demonstrating its asymptotic normality and semiparametric efficiency. Finally, we validate our design using synthetic data from a clinical trial on cirrhosis. | Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments | [
"Yanping Li",
"Jingshen Wang",
"Waverly Wei"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4VWnC5unAV | @inproceedings{
collins-woodfin2024the,
title={The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms},
author={Elizabeth Collins-Woodfin and Inbar Seroussi and Bego{\~n}a Garc{\'\i}a Malaxechebarr{\'\i}a and Andrew Mackenzie and Elliot Paquette and Courtney Paquette},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4VWnC5unAV}
} | We develop a framework for analyzing the training and learning rate dynamics on a large class of high-dimensional optimization problems, which we call the high line, trained using one-pass stochastic gradient descent (SGD) with adaptive learning rates. We give exact expressions for the risk and learning rate curves in terms of a deterministic solution to a system of ODEs. We then investigate in detail two adaptive learning rates -- an idealized exact line search and AdaGrad-Norm -- on the least squares problem. When the data covariance matrix has strictly positive eigenvalues, this idealized exact line search strategy can exhibit arbitrarily slower convergence when compared to the optimal fixed learning rate with SGD. Moreover we exactly characterize the limiting learning rate (as time goes to infinity) for line search in the setting where the data covariance has only two distinct eigenvalues. For noiseless targets, we further demonstrate that the AdaGrad-Norm learning rate converges to a deterministic constant inversely proportional to the average eigenvalue of the data covariance matrix, and identify a phase transition when the covariance density of eigenvalues follows a power law distribution. We provide
our code for evaluation at https://github.com/amackenzie1/highline2024. | The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms | [
"Elizabeth Collins-Woodfin",
"Inbar Seroussi",
"Begoña García Malaxechebarría",
"Andrew Mackenzie",
"Elliot Paquette",
"Courtney Paquette"
] | NeurIPS.cc/2024/Conference | 2405.19585 | [
"https://github.com/amackenzie1/highline2024"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4VL5QWQFBV | @inproceedings{
huang2024disentangled,
title={Disentangled Style Domain for Implicit \$z\$-Watermark Towards Copyright Protection},
author={Junqiang Huang and Zhaojun Guo and Ge Luo and Zhenxing Qian and Sheng Li and Xinpeng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4VL5QWQFBV}
} | Text-to-image models have shown surprising performance in high-quality image generation, while also raising intensified concerns about the unauthorized usage of personal dataset in training and personalized fine-tuning. Recent approaches, embedding watermarks, introducing perturbations, and inserting backdoors into datasets, rely on adding minor information vulnerable to adversarial purification, limiting their ability to detect unauthorized data usage. In this paper, we introduce a novel implicit Zero-Watermarking scheme that first utilizes the disentangled style domain to detect unauthorized dataset usage in text-to-image models. Specifically, our approach generates the watermark from the disentangled style domain, enabling self-generalization and mutual exclusivity within the style domain anchored by protected units. The domain achieves the maximum concealed offset of probability distribution through both the injection of identifier $z$ and dynamic contrastive learning, facilitating the structured delineation of dataset copyright boundaries for multiple sources of styles and contents. Additionally, we introduce the concept of watermark distribution to establish a verification mechanism for copyright ownership of hybrid or partial infringements, addressing deficiencies in the traditional mechanism of dataset copyright ownership for AI mimicry. Notably, our method achieved One-Sample-Verification for copyright ownership in AI mimic generations. The code is available at： [https://github.com/Hlufies/ZWatermarking](https://github.com/Hlufies/ZWatermarking) | Disentangled Style Domain for Implicit z-Watermark Towards Copyright Protection | [
"Junqiang Huang",
"Zhaojun Guo",
"Ge Luo",
"Zhenxing Qian",
"Sheng Li",
"Xinpeng Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4UvMOnZMam | @inproceedings{
elelimy2024realtime,
title={Real-Time Recurrent Learning using Trace Units in Reinforcement Learning},
author={Esraa Elelimy and Adam White and Michael Bowling and Martha White},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4UvMOnZMam}
} | Recurrent Neural Networks (RNNs) are used to learn representations in partially observable environments. For agents that learn online and continually interact with the environment, it is desirable to train RNNs with real-time recurrent learning (RTRL); unfortunately, RTRL is prohibitively expensive for standard RNNs. A promising direction is to use linear recurrent architectures (LRUs), where dense recurrent weights are replaced with a complex-valued diagonal, making RTRL efficient. In this work, we build on these insights to provide a lightweight but effective approach for training RNNs in online RL. We introduce Recurrent Trace Units (RTUs), a small modification on LRUs that we nonetheless find to have significant performance benefits over LRUs when trained with RTRL. We find RTUs significantly outperform GRUs and Transformers across several partially observable environments while using significantly less computation. | Real-Time Recurrent Learning using Trace Units in Reinforcement Learning | [
"Esraa Elelimy",
"Adam White",
"Michael Bowling",
"Martha White"
] | NeurIPS.cc/2024/Conference | 2409.01449 | [
"https://github.com/esraaelelimy/rtus"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4Un2TD9bNe | @inproceedings{
zhang2024rethinking,
title={Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models},
author={Hanxiao Zhang and Lin JU and Chan Wu and Jinjing Huang and Youshao Xiao and Zhenglei Zhou and Zhiming fan and Zhaoxin Huan and Siyuan Li and Fanzhuang Meng and Lei Liang and Xiaolu Zhang and JUN ZHOU},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4Un2TD9bNe}
} | Recently, various strategies for distributed training of large language models (LLMs) have been proposed.
By categorizing them into basic strategies and composite strategies, we have discovered that existing basic strategies provide limited options in specific scenarios, leaving considerable room for optimization in training speed.
In this paper, we rethink the impact of memory and communication costs on the training speed of LLMs, taking into account the impact of intra- and inter-group communication performance disparities, and then propose a new set of basic strategies named the \textbf{Pa}rtial \textbf{R}edundancy \textbf{O}ptimizer (PaRO).
PaRO Data Parallelism (PaRO-DP) accelerates LLM training through refined model state partitioning and tailored training procedures. At the same time, PaRO Collective Communications (PaRO-CC) speeds up collective communication operations by rearranging the topology. We also propose a guideline for choosing different DP strategies based on simple quantitative calculations, which yields minimal ranking errors.
Our experiments demonstrate that PaRO improves the training speed of LLMs by up to 266\% that of ZeRO-3 as basic DP strategies.
Moreover, employing PaRO-CC independently for model parallel strategies, such as Megatron, can also boost the training speed by 17\%. | Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models | [
"Hanxiao Zhang",
"Lin JU",
"Chan Wu",
"Jinjing Huang",
"Youshao Xiao",
"Zhenglei Zhou",
"Zhiming fan",
"Zhaoxin Huan",
"Siyuan Li",
"Fanzhuang Meng",
"Lei Liang",
"Xiaolu Zhang",
"JUN ZHOU"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4UReW4Ez6s | @inproceedings{
hu2024provably,
title={Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes},
author={Jerry Yao-Chieh Hu and Dennis Wu and Han Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4UReW4Ez6s}
} | We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories.
We present a tight analysis by establishing a connection between the memory configuration of KHMs and spherical codes from information theory.
Specifically, we treat the stored memory set as a specialized spherical code.
This enables us to cast the memorization problem in KHMs into a point arrangement problem on a hypersphere.
We show that the optimal capacity of KHMs occurs when the feature space allows memories to form an optimal spherical code.
This unique perspective leads to:
1. An analysis of how KHMs achieve optimal memory capacity, and identify corresponding necessary conditions.
Importantly, we establish an upper capacity bound that matches the well-known exponential lower bound in the literature.
This provides the first tight and optimal asymptotic memory capacity for modern Hopfield models.
2. A sub-linear time algorithm $\mathtt{U}\text{-}\mathtt{Hop}$+ to reach KHMs' optimal capacity.
3. An analysis of the scaling behavior of the required feature dimension relative to the number of stored memories.
These efforts improve both the retrieval capability of KHMs and the representation learning of corresponding transformers.
Experimentally, we provide thorough numerical results to back up theoretical findings. | Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes | [
"Jerry Yao-Chieh Hu",
"Dennis Wu",
"Han Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4U18ZoRXTD | @inproceedings{
bhosale2024avgs,
title={{AV}-{GS}: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis},
author={Swapnil Bhosale and Haosen Yang and Diptesh Kanojia and Jiankang Deng and Xiatian Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4U18ZoRXTD}
} | Novel view acoustic synthesis (NVAS) aims to render binaural audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition for synthesizing binaural audio. However, in addition to low efficiency originating from heavy NeRF rendering, these methods all have a limited ability of characterizing the entire scene environment such as room geometry, material properties, and the spatial relation between the listener and sound source. To address these issues, we propose a novel Audio-Visual Gaussian Splatting (AV-GS) model. To obtain a material-aware and geometry-aware condition for audio synthesis, we learn an explicit point-based scene representation with audio-guidance parameters on locally initialized Gaussian points, taking into account the space relation from the listener and sound source. To make the visual scene model audio adaptive, we propose a point densification and pruning strategy to optimally distribute the Gaussian points, with the per-point contribution in sound propagation (e.g., more points needed for texture-less wall surfaces as they affect sound path diversion). Extensive experiments validate the superiority of our AV-GS over existing alternatives on the real-world RWAS and simulation-based SoundSpaces datasets. Project page: \url{https://surrey-uplab.github.io/research/avgs/} | AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis | [
"Swapnil Bhosale",
"Haosen Yang",
"Diptesh Kanojia",
"Jiankang Deng",
"Xiatian Zhu"
] | NeurIPS.cc/2024/Conference | 2406.08920 | [
""
] | https://huggingface.co/papers/2406.08920 | 2 | 7 | 1 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4TlUE0ufiz | @inproceedings{
liang2024introspective,
title={Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity},
author={Kaiqu Liang and Zixu Zhang and Jaime Fern{\'a}ndez Fisac},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4TlUE0ufiz}
} | Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions through proper grounding. However, LLM hallucination may result in robots confidently executing plans that are misaligned with user goals or even unsafe in critical scenarios. Additionally, inherent ambiguity in natural language instructions can introduce uncertainty into the LLM's reasoning and planning. We propose introspective planning, a systematic approach that guides LLMs to refine their own uncertainty in alignment with inherent task ambiguity. Our approach constructs a knowledge base containing introspective reasoning examples as post-hoc rationalizations of human-selected safe and compliant plans, which are retrieved during deployment. Evaluations on three tasks, including a new safe mobile manipulation benchmark, indicate that introspection substantially improves both compliance and safety over state-of-the-art LLM-based planning methods. Additionally, we empirically show that introspective planning, in combination with conformal prediction, achieves tighter confidence bounds, maintaining statistical success guarantees while minimizing unnecessary user clarification requests. | Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity | [
"Kaiqu Liang",
"Zixu Zhang",
"Jaime Fernández Fisac"
] | NeurIPS.cc/2024/Conference | 2402.06529 | [
"https://github.com/kevinliang888/IntroPlan"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4TENzBftZR | @inproceedings{
wu2024ivideogpt,
title={iVideo{GPT}: Interactive Video{GPT}s are Scalable World Models},
author={Jialong Wu and Shaofeng Yin and Ningya Feng and Xu He and Dong Li and Jianye HAO and Mingsheng Long},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4TENzBftZR}
} | World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interactivity poses challenges in harnessing recent advancements in video generative models for developing world models at scale. This work introduces Interactive VideoGPT (iVideoGPT), a scalable autoregressive transformer framework that integrates multimodal signals—visual observations, actions, and rewards—into a sequence of tokens, facilitating an interactive experience of agents via next-token prediction. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations. Leveraging its scalable architecture, we are able to pre-train iVideoGPT on millions of human and robotic manipulation trajectories, establishing a versatile foundation that is adaptable to serve as interactive world models for a wide range of downstream tasks. These include action-conditioned video prediction, visual planning, and model-based reinforcement learning, where iVideoGPT achieves competitive performance compared with state-of-the-art methods. Our work advances the development of interactive general world models, bridging the gap between generative video models and practical model-based reinforcement learning applications. Code and pre-trained models are available at https://thuml.github.io/iVideoGPT. | iVideoGPT: Interactive VideoGPTs are Scalable World Models | [
"Jialong Wu",
"Shaofeng Yin",
"Ningya Feng",
"Xu He",
"Dong Li",
"Jianye HAO",
"Mingsheng Long"
] | NeurIPS.cc/2024/Conference | 2405.15223 | [
"https://github.com/thuml/iVideoGPT"
] | https://huggingface.co/papers/2405.15223 | 3 | 12 | 1 | 7 | [
"thuml/ivideogpt-oxe-64-act-free"
] | [] | [] | [
"thuml/ivideogpt-oxe-64-act-free"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4SAR7IRqmB | @inproceedings{
bharti2024on,
title={On the Complexity of Teaching a Family of Linear Behavior Cloning Learners},
author={Shubham Kumar Bharti and Stephen Wright and Adish Singla and Jerry Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4SAR7IRqmB}
} | We study optimal teaching for a family of Behavior Cloning learners that learn using a linear hypothesis class. In this setup, a knowledgeable teacher can demonstrate a dataset of state and action tuples and is required to teach an optimal policy to an entire family of BC learners using the smallest possible dataset. We analyze the linear family and design a novel teaching algorithm called `TIE' that achieves the instance optimal Teaching Dimension for the entire family. However, we show that this problem is NP-hard for action spaces with $|\mathcal{A}| > 2$ and provide an efficient approximation algorithm with a $\log(|\mathcal{A}| - 1)$ guarantee on the optimal teaching size. We present empirical results to demonstrate the effectiveness of our algorithm and compare it to various baselines in different teaching environments. | On the Complexity of Teaching a Family of Linear Behavior Cloning Learners | [
"Shubham Kumar Bharti",
"Stephen Wright",
"Adish Singla",
"Jerry Zhu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4OJdZhcwBb | @inproceedings{
adkins2024a,
title={A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning},
author={Jacob Adkins and Michael Bowling and Adam White},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4OJdZhcwBb}
} | The performance of modern reinforcement learning algorithms critically relies
on tuning ever increasing numbers of hyperparameters. Often, small changes in
a hyperparameter can lead to drastic changes in performance, and different environments require very different hyperparameter settings to achieve state-of-the-art
performance reported in the literature. We currently lack a scalable and widely
accepted approach to characterizing these complex interactions. This work proposes a new empirical methodology for studying, comparing, and quantifying the
sensitivity of an algorithm’s performance to hyperparameter tuning for a given set
of environments. We then demonstrate the utility of this methodology by assessing
the hyperparameter sensitivity of several commonly used normalization variants of
PPO. The results suggest that several algorithmic performance improvements may,
in fact, be a result of an increased reliance on hyperparameter tuning. | A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning | [
"Jacob Adkins",
"Michael Bowling",
"Adam White"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4NQ24cHnOi | @inproceedings{
chen2024private,
title={Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust},
author={Hongjie Chen and Jingqiu Ding and Yiding Hua and David Steurer},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4NQ24cHnOi}
} | We give the first polynomial-time, differentially node-private, and robust algorithm for estimating the edge density of Erdős-Rényi random graphs and their generalization, inhomogeneous random graphs. We further prove information-theoretical lower bounds, showing that the error rate of our algorithm is optimal up to logarithmic factors. Previous algorithms incur either exponential running time or suboptimal error rates.
Two key ingredients of our algorithm are (1) a new sum-of-squares algorithm for robust edge density estimation, and (2) the reduction from privacy to robustness based on sum-of-squares exponential mechanisms due to Hopkins et al. (STOC 2023). | Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust | [
"Hongjie Chen",
"Jingqiu Ding",
"Yiding Hua",
"David Steurer"
] | NeurIPS.cc/2024/Conference | 2405.16663 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=4NJBV6Wp0h | @inproceedings{
panickssery2024llm,
title={{LLM} Evaluators Recognize and Favor Their Own Generations},
author={Arjun Panickssery and Samuel R. Bowman and Shi Feng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4NJBV6Wp0h}
} | Self-evaluation using large language models (LLMs) has proven valuable not only in benchmarking but also methods like reward modeling, constitutional AI, and self-refinement. But new biases are introduced due to the same LLM acting as both the evaluator and the evaluatee. One such bias is self-preference, where an LLM evaluator scores its own outputs higher than others’ while human annotators consider them of equal quality. But do LLMs actually recognize their own outputs when they give those texts higher scores, or is it just a coincidence? In this paper, we investigate if self-recognition capability contributes to self-preference. We discover that, out of the box, LLMs such as GPT-4 and Llama 2 have non-trivial accuracy at distinguishing themselves from other LLMs and humans. By finetuning LLMs, we discover a linear correlation between self-recognition capability and the strength of self-preference bias; using controlled experiments, we show that the causal explanation resists straightforward confounders. We discuss how self-recognition can interfere with unbiased evaluations and AI safety more generally. | LLM Evaluators Recognize and Favor Their Own Generations | [
"Arjun Panickssery",
"Samuel R. Bowman",
"Shi Feng"
] | NeurIPS.cc/2024/Conference | 2404.13076 | [
""
] | https://huggingface.co/papers/2404.13076 | 0 | 1 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=4NGrHrhJPx | @inproceedings{
qin2024the,
title={The Dormant Neuron Phenomenon in Multi-Agent Reinforcement Learning Value Factorization},
author={Haoyuan Qin and Chennan Ma and Mian Deng and Zhengzhu Liu and Songzhu Mei and Xinwang Liu and Cheng Wang and Siqi Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4NGrHrhJPx}
} | In this work, we study the dormant neuron phenomenon in multi-agent reinforcement learning value factorization, where the mixing network suffers from reduced network expressivity caused by an increasing number of inactive neurons. We demonstrate the presence of the dormant neuron phenomenon across multiple environments and algorithms, and show that this phenomenon negatively affects the learning process. We show that dormant neurons correlates with the existence of over-active neurons, which have large activation scores. To address the dormant neuron issue, we propose ReBorn, a simple but effective method that transfers the weights from over-active neurons to dormant neurons. We theoretically show that this method can ensure the learned action preferences are not forgotten after the weight-transferring procedure, which increases learning effectiveness. Our extensive experiments reveal that ReBorn achieves promising results across various environments and improves the performance of multiple popular value factorization approaches. The source code of ReBorn is available in \url{https://github.com/xmu-rl-3dv/ReBorn}. | The Dormant Neuron Phenomenon in Multi-Agent Reinforcement Learning Value Factorization | [
"Haoyuan Qin",
"Chennan Ma",
"Mian Deng",
"Zhengzhu Liu",
"Songzhu Mei",
"Xinwang Liu",
"Cheng Wang",
"Siqi Shen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4NGlu45uyt | @inproceedings{
even2024aligning,
title={Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem},
author={Mathieu Even and Luca Ganassali and Jakob Maier and Laurent Massouli{\'e}},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4NGlu45uyt}
} | The Procrustes-Wasserstein problem consists in matching two high-dimensional point clouds in an unsupervised setting, and has many applications in natural language processing and computer vision.
We consider a planted model with two datasets $X,Y$ that consist of $n$ datapoints in $\mathbb{R}^d$, where $Y$ is a noisy version of $X$, up to an orthogonal transformation and a relabeling of the data points.
This setting is related to the graph alignment problem in geometric models.
In this work, we focus on the euclidean transport cost between the point clouds as a measure of performance for the alignment. We first establish information-theoretic results, in the high ($d \gg \log n$) and low ($d \ll \log n$) dimensional regimes.
We then study computational aspects and propose the ‘Ping-Pong algorithm', alternatively estimating the orthogonal transformation and the relabeling, initialized via a Franke-Wolfe convex relaxation. We give sufficient conditions for the method to retrieve the planted signal after one single step. We provide experimental results to compare the proposed approach with the state-of-the-art method of Grave et al. (2019). | Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem | [
"Mathieu Even",
"Luca Ganassali",
"Jakob Maier",
"Laurent Massoulié"
] | NeurIPS.cc/2024/Conference | 2405.14532 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4M9f8VMt2C | @inproceedings{
wei2024longform,
title={Long-form factuality in large language models},
author={Jerry Wei and Chengrun Yang and Xinying Song and Yifeng Lu and Nathan Zixia Hu and Jie Huang and Dustin Tran and Daiyi Peng and Ruibo Liu and Da Huang and Cosmo Du and Quoc V Le},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4M9f8VMt2C}
} | Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics. To benchmark a model’s long-form factuality in open domains, we first use GPT-4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics. We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into a set of individual facts and to evaluate the accuracy of each fact using a multi-step reasoning process comprising sending search queries to Google Search and determining whether a fact is supported by the search results. Furthermore, we propose extending F1 score as an aggregated metric for long-form factuality. To do so, we balance the percentage of supported facts in a response (precision) with the percentage of provided facts relative to a hyperparameter representing a user’s preferred response length (recall).
Empirically, we demonstrate that LLM agents can outperform crowdsourced human annotators—on a set of∼16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times cheaper than human annotators. We also benchmark thirteen language models on LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding that larger language models generally achieve better long-form factuality. LongFact, SAFE, and all experimental code are available at https://github.com/google-deepmind/long-form-factuality. | Long-form factuality in large language models | [
"Jerry Wei",
"Chengrun Yang",
"Xinying Song",
"Yifeng Lu",
"Nathan Zixia Hu",
"Jie Huang",
"Dustin Tran",
"Daiyi Peng",
"Ruibo Liu",
"Da Huang",
"Cosmo Du",
"Quoc V Le"
] | NeurIPS.cc/2024/Conference | 2403.18802 | [
"https://github.com/google-deepmind/long-form-factuality"
] | https://huggingface.co/papers/2403.18802 | 1 | 24 | 2 | 11 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4Lkzghiep1 | @inproceedings{
ahmadi2024strategic,
title={Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification},
author={Saba Ahmadi and Kunhe Yang and Hanrui Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4Lkzghiep1}
} | We study the problem of online binary classification in settings where strategic agents can modify their observable features to receive a positive classification. We model the set of feasible manipulations by a directed graph over the feature space, and assume the learner only observes the manipulated features instead of the original ones. We introduce the Strategic Littlestone Dimension, a new combinatorial measure that captures the joint complexity of the hypothesis class and the manipulation graph. We demonstrate that it characterizes the instance-optimal mistake bounds for deterministic learning algorithms in the realizable setting. We also achieve improved regret in the agnostic setting by a refined agnostic-to-realizable reduction that accounts for the additional challenge of not observing agents' original features. Finally, we relax the assumption that the learner knows the manipulation graph, instead assuming their knowledge is captured by a family of graphs. We derive regret bounds in both the realizable setting where all agents manipulate according to the same graph within the graph family, and the agnostic setting where the manipulation graphs are chosen adversarially and not consistently modeled by a single graph in the family. | Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification | [
"Saba Ahmadi",
"Kunhe Yang",
"Hanrui Zhang"
] | NeurIPS.cc/2024/Conference | 2407.11619 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4Ktifp48WD | @inproceedings{
ghazi2024differentially,
title={Differentially Private Optimization with Sparse Gradients},
author={Badih Ghazi and Crist{\'o}bal A Guzm{\'a}n and Pritish Kamath and Ravi Kumar and Pasin Manurangsi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4Ktifp48WD}
} | Motivated by applications of large embedding models, we study differentially private (DP) optimization problems under sparsity of _individual_ gradients. We start with new near-optimal bounds for the classic mean estimation problem but with sparse data, improving upon existing algorithms particularly for the high-dimensional regime. The corresponding lower bounds are based on a novel block-diagonal construction that is combined with existing DP mean estimation lower bounds.
Next, we obtain pure- and approximate-DP algorithms with almost optimal rates
for stochastic convex optimization with sparse gradients; the former represents the first nearly dimension-independent rates for this problem. Furthermore, by introducing novel analyses of bias reduction in mean estimation and randomly-stopped biased SGD we obtain nearly dimension-independent rates for near-stationary points for the empirical risk in nonconvex settings under approximate-DP. | Differentially Private Optimization with Sparse Gradients | [
"Badih Ghazi",
"Cristóbal A Guzmán",
"Pritish Kamath",
"Ravi Kumar",
"Pasin Manurangsi"
] | NeurIPS.cc/2024/Conference | 2404.10881 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4KeSvAvNMr | @inproceedings{
beltran-velez2024treeffuser,
title={Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees},
author={Nicolas Beltran-Velez and Alessandro Antonio Grande and Achille Nazaret and Alp Kucukelbir and David Blei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4KeSvAvNMr}
} | Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume parametric responses, such as Gaussian or Poisson distributions. When these assumptions fail, such models lead to bad predictions and poorly calibrated uncertainty. In this paper, we propose Treeffuser, an easy-to-use method for probabilistic prediction on tabular data. The idea is to learn a conditional diffusion model where the score function is estimated using gradient-boosted trees. The conditional diffusion model makes Treeffuser flexible and non-parametric, while the gradient-boosted trees make it robust and easy to train on CPUs. Treeffuser learns well-calibrated predictive distributions and can handle a wide range of regression tasks---including those with multivariate, multimodal, and skewed responses. We study Treeffuser on synthetic and real data and show that it outperforms existing methods, providing better calibrated probabilistic predictions. We further demonstrate its versatility with an application to inventory allocation under uncertainty using sales data from Walmart. We implement Treeffuser in https://github.com/blei-lab/treeffuser. | Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees | [
"Nicolas Beltran-Velez",
"Alessandro Antonio Grande",
"Achille Nazaret",
"Alp Kucukelbir",
"David Blei"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4I2aEav51N | @inproceedings{
durfee2024instancespecific,
title={Instance-Specific Asymmetric Sensitivity in Differential Privacy},
author={David Durfee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4I2aEav51N}
} | We provide a new algorithmic framework for differentially private estimation of general functions that adapts to the hardness of the underlying dataset. We build upon previous work that gives a paradigm for selecting an output through the exponential mechanism based upon closeness of the inverse to the underlying dataset, termed the inverse sensitivity mechanism. Our framework will slightly modify the closeness metric and instead give a simple and efficient application of the sparse vector technique. While the inverse sensitivity mechanism was shown to be instance optimal, it was only with respect to a class of unbiased mechanisms such that the most likely outcome matches the underlying data. We break this assumption in order to more naturally navigate the bias-variance tradeoff, which will also critically allow for extending our method to unbounded data. In consideration of this tradeoff, we provide theoretical guarantees and empirical validation that our technique will be particularly effective when the distances to the underlying dataset are asymmetric. This asymmetry is inherent to a range of important problems including fundamental statistics such as variance, as well as commonly used machine learning performance metrics for both classification and regression tasks. We efficiently instantiate our method in $O(n)$ time for these problems and empirically show that our techniques will give substantially improved differentially private estimations. | Instance-Specific Asymmetric Sensitivity in Differential Privacy | [
"David Durfee"
] | NeurIPS.cc/2024/Conference | 2311.14681 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4GP7S7U0lJ | @inproceedings{
zhang2024learnability,
title={Learnability Matters: Active Learning for Video Captioning},
author={Yiqian Zhang and Buyu Liu and Jun Bao and Qiang Huang and Min Zhang and Jun Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4GP7S7U0lJ}
} | This work focuses on the active learning in video captioning. In particular, we propose to address the learnability problem in active learning, which has been brought up by collective outliers in video captioning and neglected in the literature. To start with, we conduct a comprehensive study of collective outliers, exploring their hard-to-learn property and concluding that ground truth inconsistency is one of the main causes. Motivated by this, we design a novel active learning algorithm that takes three complementary aspects, namely learnability, diversity, and uncertainty, into account. Ideally, learnability is reflected by ground truth consistency. Under the active learning scenario where ground truths are not available until human involvement, we measure the consistency on estimated ground truths, where predictions from off-the-shelf models are utilized as approximations to ground truths. These predictions are further used to estimate sample frequency and reliability, evincing the diversity and uncertainty respectively. With the help of our novel caption-wise active learning protocol, our algorithm is capable of leveraging knowledge from humans in a more effective yet intellectual manner. Results on publicly available video captioning datasets with diverse video captioning models demonstrate that our algorithm outperforms SOTA active learning methods by a large margin, e.g. we achieve about 103% of full performance on CIDEr with 25% of human annotations on MSR-VTT. | Learnability Matters: Active Learning for Video Captioning | [
"Yiqian Zhang",
"Buyu Liu",
"Jun Bao",
"Qiang Huang",
"Min Zhang",
"Jun Yu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4G2DN4Kjk1 | @inproceedings{
jain2024linear,
title={Linear Regression using Heterogeneous Data Batches},
author={Ayush Jain and Rajat Sen and Weihao Kong and Abhimanyu Das and Alon Orlitsky},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4G2DN4Kjk1}
} | In many learning applications, data are collected from multiple sources, each providing a \emph{batch} of samples that by itself is insufficient to learn its input-output relationship. A common approach assumes that the sources fall in one of several unknown subgroups, each with an unknown input distribution and input-output relationship. We consider one of this setup's most fundamental and important manifestations where the output is a noisy linear combination of the inputs, and there are $k$ subgroups, each with its own regression vector. Prior work [KSS$^+$20] showed that with abundant small-batches, the regression vectors can be learned with only few, $\tilde\Omega( k^{3/2})$, batches of medium-size with $\tilde\Omega(\sqrt k)$ samples each. However, the paper requires that the input distribution for all $k$ subgroups be isotropic Gaussian, and states that removing this assumption is an ``interesting and challenging problem". We propose a novel gradient-based algorithm that improves on the existing results in several ways. It extends the applicability of the algorithm by: (1) allowing the subgroups' underlying input distributions to be different, unknown, and heavy-tailed; (2) recovering all subgroups followed by a significant proportion of batches even for infinite $k$; (3) removing the separation requirement between the regression vectors; (4) reducing the number of batches and allowing smaller batch sizes. | Linear Regression using Heterogeneous Data Batches | [
"Ayush Jain",
"Rajat Sen",
"Weihao Kong",
"Abhimanyu Das",
"Alon Orlitsky"
] | NeurIPS.cc/2024/Conference | 2309.01973 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=4DcpFagQ9e | @inproceedings{
lukoianov2024score,
title={Score Distillation via Reparametrized {DDIM}},
author={Artem Lukoianov and Haitz S{\'a}ez de Oc{\'a}riz Borde and Kristjan Greenewald and Vitor Campagnolo Guizilini and Timur Bagautdinov and Vincent Sitzmann and Justin Solomon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4DcpFagQ9e}
} | While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cartoon-like, over-smoothed shapes. To help explain this discrepancy, we show that the image guidance used in Score Distillation can be understood as the velocity field of a 2D denoising generative process, up to the choice of a noise term. In particular, after a change of variables, SDS resembles a high-variance version of Denoising Diffusion Implicit Models (DDIM) with a differently-sampled noise term: SDS introduces noise i.i.d. randomly at each step, while DDIM infers it from the previous noise predictions. This excessive variance can lead to over-smoothing and unrealistic outputs. We show that a better noise approximation can be recovered by inverting DDIM in each SDS update step. This modification makes SDS's generative process for 2D images almost identical to DDIM. In 3D, it removes over-smoothing, preserves higher-frequency detail, and brings the generation quality closer to that of 2D samplers. Experimentally, our method achieves better or similar 3D generation quality compared to other state-of-the-art Score Distillation methods, all without training additional neural networks or multi-view supervision, and providing useful insights into relationship between 2D and 3D asset generation with diffusion models. | Score Distillation via Reparametrized DDIM | [
"Artem Lukoianov",
"Haitz Sáez de Ocáriz Borde",
"Kristjan Greenewald",
"Vitor Campagnolo Guizilini",
"Timur Bagautdinov",
"Vincent Sitzmann",
"Justin Solomon"
] | NeurIPS.cc/2024/Conference | 2405.15891 | [
""
] | https://huggingface.co/papers/2405.15891 | 1 | 3 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=4DHoSjET4R | @inproceedings{
deng2024efficiency,
title={Efficiency of the First-Price Auction in the Autobidding World},
author={Yuan Deng and Jieming Mao and Vahab Mirrokni and Hanrui Zhang and Song Zuo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4DHoSjET4R}
} | We study the price of anarchy of first-price auctions in the autobidding world, where bidders can be either utility maximizers (i.e., traditional bidders) or value maximizers (i.e., autobidders). We show that with autobidders only, the price of anarchy of first-price auctions is $1/2$, and with both kinds of bidders, the price of anarchy degrades to about $0.457$ (the precise number is given by an optimization). These results complement the recent result by [Jin and Lu, 2022] showing that the price of anarchy of first-price auctions with traditional bidders is $1 - 1/e^2$. We further investigate a setting where the seller can utilize machine-learned advice to improve the efficiency of the auctions. There, we show that as the accuracy of the advice increases, the price of anarchy improves smoothly from about $0.457$ to $1$. | Efficiency of the First-Price Auction in the Autobidding World | [
"Yuan Deng",
"Jieming Mao",
"Vahab Mirrokni",
"Hanrui Zhang",
"Song Zuo"
] | NeurIPS.cc/2024/Conference | 2208.10650 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4DA5vaPHFb | @inproceedings{
buzun2024expectile,
title={Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport},
author={Nazar Buzun and Maksim Bobrin and Dmitry V. Dylov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4DA5vaPHFb}
} | We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularization on dual Kantorovich potentials. The main bottleneck of existing NOT solvers is associated with the procedure of finding a near-exact approximation of the conjugate operator (i.e., the c-transform), which is done either by optimizing over non-convex max-min objectives or by the computationally intensive fine-tuning of the initial approximated prediction. We resolve both issues by proposing a new theoretically justified loss in the form of expectile regularization which enforces binding conditions on the learning process of the dual potentials. Such a regularization provides the upper bound estimation over the distribution of possible conjugate potentials and makes the learning stable, completely eliminating the need for additional extensive fine-tuning. Proposed method, called Expectile-Regularized Neural Optimal Transport (ENOT), outperforms previous state-of-the-art approaches in the established Wasserstein-2 benchmark tasks by a large margin (up to a 3-fold improvement in quality and up to a 10-fold improvement in runtime). Moreover, we showcase performance of ENOT for various cost functions in different tasks, such as image generation, demonstrating generalizability and robustness of the proposed algorithm. | Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport | [
"Nazar Buzun",
"Maksim Bobrin",
"Dmitry V. Dylov"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=4D7hnJ9oM6 | @inproceedings{
osowiechi2024watt,
title={{WATT}: Weight Average Test Time Adaptation of {CLIP}},
author={David OSOWIECHI and Mehrdad Noori and Gustavo Adolfo Vargas Hakim and Moslem Yazdanpanah and Ali Bahri and Milad Cheraghalikhani and Sahar Dastani and Farzad Beizaee and Ismail Ben Ayed and Christian Desrosiers},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4D7hnJ9oM6}
} | Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performances for zero-shot image classification, yet their generalization capability may still be seriously challenged when confronted to domain shifts. In response, we present Weight Average Test-Time Adaptation (WATT) of CLIP, a new approach facilitating full test-time adaptation (TTA) of this VLM. Our method employs a diverse set of templates for text prompts, augmenting the existing framework of CLIP. Predictions are utilized as pseudo labels for model updates, followed by weight averaging to consolidate the learned information globally. Furthermore, we introduce a text ensemble strategy, enhancing the overall test performance by aggregating diverse textual cues.
Our findings underscore the effectiveness of WATT across diverse datasets, including CIFAR-10-C, CIFAR-10.1, CIFAR-100-C, VisDA-C, and several other challenging datasets, effectively covering a wide range of domain shifts. Notably, these enhancements are achieved without the need for additional model transformations or trainable modules. Moreover, compared to other TTA methods, our approach can operate effectively with just a single image. The code is available at: https://github.com/Mehrdad-Noori/WATT. | WATT: Weight Average Test Time Adaptation of CLIP | [
"David OSOWIECHI",
"Mehrdad Noori",
"Gustavo Adolfo Vargas Hakim",
"Moslem Yazdanpanah",
"Ali Bahri",
"Milad Cheraghalikhani",
"Sahar Dastani",
"Farzad Beizaee",
"Ismail Ben Ayed",
"Christian Desrosiers"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/mehrdad-noori/watt"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4D7haH4pdR | @inproceedings{
chen2024bias,
title={Bias Detection via Signaling},
author={Yiling Chen and Tao Lin and Ariel D. Procaccia and Aaditya Ramdas and Itai Shapira},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4D7haH4pdR}
} | We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased towards their own prior. In our model, biased agents form posterior beliefs that are a convex combination of their prior and the Bayesian posterior, where the more biased an agent is, the closer their posterior is to the prior. Since we often cannot observe the agent's beliefs directly, we take an approach inspired by *information design*. Specifically, we measure an agent's bias by designing a *signaling scheme* and observing the actions they take in response to different signals, assuming that they are maximizing their own expected utility; our goal is to detect bias with a minimum number of signals. Our main results include a characterization of scenarios where a single signal suffices and a computationally efficient algorithm to compute optimal signaling schemes. | Bias Detection via Signaling | [
"Yiling Chen",
"Tao Lin",
"Ariel D. Procaccia",
"Aaditya Ramdas",
"Itai Shapira"
] | NeurIPS.cc/2024/Conference | 2405.17694 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4BWlUJF0E9 | @inproceedings{
sun2024towards,
title={Towards Dynamic Message Passing on Graphs},
author={Junshu Sun and Chenxue Yang and Xiangyang Ji and Qingming Huang and Shuhui Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4BWlUJF0E9}
} | Message passing plays a vital role in graph neural networks (GNNs) for effective feature learning. However, the over-reliance on input topology diminishes the efficacy of message passing and restricts the ability of GNNs. Despite efforts to mitigate the reliance, existing study encounters message-passing bottlenecks or high computational expense problems, which invokes the demands for flexible message passing with low complexity. In this paper, we propose a novel dynamic message-passing mechanism for GNNs. It projects graph nodes and learnable pseudo nodes into a common space with measurable spatial relations between them. With nodes moving in the space, their evolving relations facilitate flexible pathway construction for a dynamic message-passing process. Associating pseudo nodes to input graphs with their measured relations, graph nodes can communicate with each other intermediately through pseudo nodes under linear complexity. We further develop a GNN model named $\mathtt{N^2}$ based on our dynamic message-passing mechanism. $\mathtt{N^2}$ employs a single recurrent layer to recursively generate the displacements of nodes and construct optimal dynamic pathways. Evaluation on eighteen benchmarks demonstrates the superior performance of $\mathtt{N^2}$ over popular GNNs. $\mathtt{N^2}$ successfully scales to large-scale benchmarks and requires significantly fewer parameters for graph classification with the shared recurrent layer. | Towards Dynamic Message Passing on Graphs | [
"Junshu Sun",
"Chenxue Yang",
"Xiangyang Ji",
"Qingming Huang",
"Shuhui Wang"
] | NeurIPS.cc/2024/Conference | 2410.23686 | [
"https://github.com/sunjss/N2"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4AuEQ1FfUf | @inproceedings{
chen2024how,
title={How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?},
author={Jun Chen and Hong Chen and Bin Gu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4AuEQ1FfUf}
} | Stochastic compositional optimization (SCO) problem constitutes a class of optimization problems characterized by the objective function with a compositional form, including the tasks with known derivatives, such as AUC maximization, and the derivative-free tasks exemplified by black-box vertical federated learning (VFL). From the learning theory perspective, the learning guarantees of SCO algorithms with known derivatives have been studied in the literature. However, the potential impacts of the derivative-free setting on the learning guarantees of SCO remains unclear and merits further investigation. This paper aims to reveal the impacts by developing a theoretical analysis for two derivative-free algorithms, black-box SCGD and SCSC. Specifically, we first provide the sharper generalization upper bounds of convex SCGD and SCSC based on a new stability analysis framework more effective than prior work under some milder conditions, which is further developed to the non-convex case using the almost co-coercivity property of smooth function. Then, we derive the learning guarantees of three black-box variants of non-convex SCGD and SCSC with additional optimization analysis. Comparing these results, we theoretically uncover the impacts that a better gradient estimation brings a tighter learning guarantee and a larger proportion of unknown gradients may lead to a stronger dependence on the gradient estimation quality. Finally, our analysis is applied to two SCO algorithms, FOO-based vertical VFL and VFL-CZOFO, to build the first learning guarantees for VFL that align with the findings of SCGD and SCSC. | How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization? | [
"Jun Chen",
"Hong Chen",
"Bin Gu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4AB54h21qG | @inproceedings{
zhang2024flexsbdd,
title={Flex{SBDD}: Structure-Based Drug Design with Flexible Protein Modeling},
author={ZAIXI ZHANG and Mengdi Wang and Qi Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4AB54h21qG}
} | Structure-based drug design (SBDD), which aims to generate 3D ligand molecules binding to target proteins, is a fundamental task in drug discovery. Existing SBDD methods typically treat protein as rigid and neglect protein structural change when binding with ligand molecules, leading to a big gap with real-world scenarios and inferior generation qualities (e.g., many steric clashes). To bridge the gap, we propose FlexSBDD, a deep generative model capable of accurately modeling the flexible protein-ligand complex structure for ligand molecule generation. FlexSBDD adopts an efficient flow matching framework and leverages E(3)-equivariant network with scalar-vector dual representation to model dynamic structural changes. Moreover, novel data augmentation schemes based on structure relaxation/sidechain repacking are adopted to boost performance. Extensive experiments demonstrate that FlexSBDD achieves state-of-the-art performance in generating high-affinity molecules and effectively modeling the protein's conformation change to increase favorable protein-ligand interactions (e.g., Hydrogen bonds) and decrease steric clashes. | FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling | [
"ZAIXI ZHANG",
"Mengdi Wang",
"Qi Liu"
] | NeurIPS.cc/2024/Conference | 2409.19645 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=4A5IQEjG8c | @inproceedings{
nguyen2024slackfree,
title={Slack-Free Spiking Neural Network Formulation for Hypergraph Minimum Vertex Cover},
author={Tam Ngoc-Bang Nguyen and Anh-Dzung Doan and zhipeng cai and Tat-Jun Chin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4A5IQEjG8c}
} | Neuromorphic computers open up the potential of energy-efficient computation using spiking neural networks (SNN), which consist of neurons that exchange spike-based information asynchronously. In particular, SNNs have shown promise in solving combinatorial optimization. Underpinning the SNN methods is the concept of energy minimization of an Ising model, which is closely related to quadratic unconstrained binary optimization (QUBO). Thus, the starting point for many SNN methods is reformulating the target problem as QUBO, then executing an SNN-based QUBO solver. For many combinatorial problems, the reformulation entails introducing penalty terms, potentially with slack variables, that implement feasibility constraints in the QUBO objective. For more complex problems such as hypergraph minimum vertex cover (HMVC), numerous slack variables are introduced which drastically increase the search domain and reduce the effectiveness of the SNN solver. In this paper, we propose a novel SNN formulation for HMVC. Rather than using penalty terms with slack variables, our SNN architecture introduces additional spiking neurons with a constraint checking and correction mechanism that encourages convergence to feasible solutions. In effect, our method obviates the need for reformulating HMVC as QUBO. Experiments on neuromorphic hardware show that our method consistently yielded high quality solutions for HMVC on real and synthetic instances where the SNN-based QUBO solver often failed, while consuming measurably less energy than global solvers on CPU. | Slack-Free Spiking Neural Network Formulation for Hypergraph Minimum Vertex Cover | [
"Tam Ngoc-Bang Nguyen",
"Anh-Dzung Doan",
"zhipeng cai",
"Tat-Jun Chin"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=49hXkwpWKA | @inproceedings{
bai2024aha,
title={{AHA}: Human-Assisted Out-of-Distribution Generalization and Detection},
author={Haoyue Bai and Jifan Zhang and Robert D Nowak},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=49hXkwpWKA}
} | Modern machine learning models deployed often encounter distribution shifts in real-world applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These shifts give rise to challenges in OOD generalization and OOD detection. This paper introduces a novel, integrated approach AHA (Adaptive Human-Assisted OOD learning) to simultaneously address both OOD generalization and detection through a human-assisted framework by labeling data in the wild. Our approach strategically labels examples within a novel maximum disambiguation region, where the number of semantic and covariate OOD data roughly equalizes. By labeling within this region, we can maximally disambiguate the two types of OOD data, thereby maximizing the utility of the fixed labeling budget. Our algorithm first utilizes a noisy binary search algorithm that identifies the maximal disambiguation region with high probability. The algorithm then continues with annotating inside the identified labeling region, reaping the full benefit of human feedback. Extensive experiments validate the efficacy of our framework. We observed that with only a few hundred human annotations, our method significantly outperforms existing state-of-the-art methods that do not involve human assistance, in both OOD generalization and OOD detection. | AHA: Human-Assisted Out-of-Distribution Generalization and Detection | [
"Haoyue Bai",
"Jifan Zhang",
"Robert D Nowak"
] | NeurIPS.cc/2024/Conference | 2410.08000 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=483IPG0HWL | @inproceedings{
ye2024reevo,
title={ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution},
author={Haoran Ye and Jiarui Wang and Zhiguang Cao and Federico Berto and Chuanbo Hua and Haeyeon Kim and Jinkyoo Park and Guojie Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=483IPG0HWL}
} | The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present Reflective Evolution (ReEvo), a novel integration of evolutionary search for efficiently exploring the heuristic space, and LLM reflections to provide verbal gradients within the space. Across five heterogeneous algorithmic types, six different COPs, and both white-box and black-box views of COPs, ReEvo yields state-of-the-art and competitive meta-heuristics, evolutionary algorithms, heuristics, and neural solvers, while being more sample-efficient than prior LHHs. | ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution | [
"Haoran Ye",
"Jiarui Wang",
"Zhiguang Cao",
"Federico Berto",
"Chuanbo Hua",
"Haeyeon Kim",
"Jinkyoo Park",
"Guojie Song"
] | NeurIPS.cc/2024/Conference | 2402.01145 | [
"https://github.com/ai4co/llm-as-hh"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=47loYmzxep | @inproceedings{
zhang2024eemfd,
title={E2E-{MFD}: Towards End-to-End Synchronous Multimodal Fusion Detection},
author={Jiaqing Zhang and Mingxiang Cao and Weiying Xie and Jie Lei and DaixunLi and Wenbo Huang and Yunsong Li and Xue Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=47loYmzxep}
} | Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their complex training processes hinder broader applications. Addressing this challenge, we introduce E2E-MFD, a novel end-to-end algorithm for multimodal fusion detection. E2E-MFD streamlines the process, achieving high performance with a single training phase. It employs synchronous joint optimization across components to avoid suboptimal solutions associated to individual tasks. Furthermore, it implements a comprehensive optimization strategy in the gradient matrix for shared parameters, ensuring convergence to an optimal fusion detection configuration. Our extensive testing on multiple public datasets reveals E2E-MFD's superior capabilities, showcasing not only visually appealing image fusion but also impressive detection outcomes, such as a 3.9\% and 2.0\% $\text{mAP}_{50}$ increase on horizontal object detection dataset M3FD and oriented object detection dataset DroneVehicle, respectively, compared to state-of-the-art approaches. | E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection | [
"Jiaqing Zhang",
"Mingxiang Cao",
"Weiying Xie",
"Jie Lei",
"DaixunLi",
"Wenbo Huang",
"Yunsong Li",
"Xue Yang"
] | NeurIPS.cc/2024/Conference | 2403.09323 | [
"https://github.com/icey-zhang/E2E-MFD"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=474M9aeI4U | @inproceedings{
wang2024cove,
title={{COVE}: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing},
author={Jiangshan Wang and Yue Ma and Jiayi Guo and Yicheng Xiao and Gao Huang and Xiu Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=474M9aeI4U}
} | Video editing is an emerging task, in which most current methods adopt the pre-trained text-to-image (T2I) diffusion model to edit the source video in a zero-shot manner. Despite extensive efforts, maintaining the temporal consistency of edited videos remains challenging due to the lack of temporal constraints in the regular T2I diffusion model. To address this issue, we propose COrrespondence-guided Video Editing (COVE), leveraging the inherent diffusion feature correspondence to achieve high-quality and consistent video editing. Specifically, we propose an efficient sliding-window-based strategy to calculate the similarity among tokens in the diffusion features of source videos, identifying the tokens with high correspondence across frames. During the inversion and denoising process, we sample the tokens in noisy latent based on the correspondence and then perform self-attention within them. To save the usage of GPU memory and accelerate the editing process, we further introduce the temporal-dimensional token merging strategy, which can effectively reduce the redundancy. COVE can be seamlessly integrated into the pre-trained T2I diffusion model without the need for extra training or optimization. Extensive experiment results demonstrate that COVE achieves the start-of-the-art performance in various video editing scenarios, outperforming existing methods both quantitatively and qualitatively. The source code will be released. | COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing | [
"Jiangshan Wang",
"Yue Ma",
"Jiayi Guo",
"Yicheng Xiao",
"Gao Huang",
"Xiu Li"
] | NeurIPS.cc/2024/Conference | 2406.08850 | [
"https://github.com/wangjiangshan0725/cove"
] | https://huggingface.co/papers/2406.08850 | 0 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=46jtDC6gXu | @inproceedings{
chen2024asyncdiff,
title={AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising},
author={Zigeng Chen and Xinyin Ma and Gongfan Fang and Zhenxiong Tan and Xinchao Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=46jtDC6gXu}
} | Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-denoising nature gives rise to high cumulative latency, thereby precluding the possibilities of parallel computation. To address this, we introduce AsyncDiff, a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. Our approach divides the cumbersome noise prediction model into multiple components, assigning each to a different device. To break the dependency chain between these components, it transforms the conventional sequential denoising into an asynchronous process by exploiting the high similarity between hidden states in consecutive diffusion steps. Consequently, each component is facilitated to compute in parallel on separate devices. The proposed strategy significantly reduces inference latency while minimally impacting the generative quality. Specifically, for the Stable Diffusion v2.1, AsyncDiff achieves a 2.7x speedup with negligible degradation and a 4.0x speedup with only a slight reduction of 0.38 in CLIP Score, on four NVIDIA A5000 GPUs. Our experiments also demonstrate AsyncDiff can be readily applied to video diffusion models with encouraging performances. | AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising | [
"Zigeng Chen",
"Xinyin Ma",
"Gongfan Fang",
"Zhenxiong Tan",
"Xinchao Wang"
] | NeurIPS.cc/2024/Conference | 2406.06911 | [
"https://github.com/czg1225/asyncdiff"
] | https://huggingface.co/papers/2406.06911 | 5 | 10 | 1 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=46V9axmOuU | @inproceedings{
fu2024apadapter,
title={{AP}-Adapter: Improving Generalization of Automatic Prompts on Unseen Text-to-Image Diffusion Models},
author={Yuchen Fu and Zhiwei Jiang and Yuliang Liu and Cong Wang and Zexuan Deng and Zhaoling Chen and Qing Gu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=46V9axmOuU}
} | Recent advancements in Automatic Prompt Optimization (APO) for text-to-image generation have streamlined user input while ensuring high-quality image output. However, most APO methods are trained assuming a fixed text-to-image model, which is impractical given the emergence of new models. To address this, we propose a novel task, model-generalized automatic prompt optimization (MGAPO), which trains APO methods on a set of known models to enable generalization to unseen models during testing. MGAPO presents significant challenges. First, we experimentally confirm the suboptimal performance of existing APO methods on unseen models. We then introduce a two-stage prompt optimization method, AP-Adapter. In the first stage, a large language model is used to rewrite the prompts. In the second stage, we propose a novel method to construct an enhanced representation space by leveraging inter-model differences. This space captures the characteristics of multiple domain models, storing them as domain prototypes. These prototypes serve as anchors to adjust prompt representations, enabling generalization to unseen models. The optimized prompt representations are subsequently used to generate conditional representations for controllable image generation. We curate a multi-modal, multi-model dataset that includes multiple diffusion models and their corresponding text-image data, and conduct experiments under a model generalization setting. The experimental results demonstrate the AP-Adapter's ability to enable the automatic prompts to generalize well to previously unseen diffusion models, generating high-quality images. | AP-Adapter: Improving Generalization of Automatic Prompts on Unseen Text-to-Image Diffusion Models | [
"Yuchen Fu",
"Zhiwei Jiang",
"Yuliang Liu",
"Cong Wang",
"Zexuan Deng",
"Zhaoling Chen",
"Qing Gu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=46Jr4sgTWa | @inproceedings{
zucchet2024recurrent,
title={Recurrent neural networks: vanishing and exploding gradients are not the end of the story},
author={Nicolas Zucchet and Antonio Orvieto},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=46Jr4sgTWa}
} | Recurrent neural networks (RNNs) notoriously struggle to learn long-term memories, primarily due to vanishing and exploding gradients. The recent success of state-space models (SSMs), a subclass of RNNs, to overcome such difficulties challenges our theoretical understanding. In this paper, we delve into the optimization challenges of RNNs and discover that, as the memory of a network increases, changes in its parameters result in increasingly large output variations, making gradient-based learning highly sensitive, even without exploding gradients. Our analysis further reveals the importance of the element-wise recurrence design pattern combined with careful parametrizations in mitigating this effect. This feature is present in SSMs, as well as in other architectures, such as LSTMs. Overall, our insights provide a new explanation for some of the difficulties in gradient-based learning of RNNs and why some architectures perform better than others. | Recurrent neural networks: vanishing and exploding gradients are not the end of the story | [
"Nicolas Zucchet",
"Antonio Orvieto"
] | NeurIPS.cc/2024/Conference | 2405.21064 | [
"https://github.com/NicolasZucchet/Vanishing-and-exploding-gradients-are-not-the-end-of-the-story"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=463TE4N8VJ | @inproceedings{
wang2024dcdepth,
title={{DCD}epth: Progressive Monocular Depth Estimation in Discrete Cosine Domain},
author={Kun Wang and Zhiqiang Yan and Junkai Fan and Wanlu Zhu and Xiang Li and Jun Li and Jian Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=463TE4N8VJ}
} | In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth. | DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain | [
"Kun Wang",
"Zhiqiang Yan",
"Junkai Fan",
"Wanlu Zhu",
"Xiang Li",
"Jun Li",
"Jian Yang"
] | NeurIPS.cc/2024/Conference | 2410.14980 | [
"https://github.com/w2kun/dcdepth"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=44WWOW4GPF | @inproceedings{
linden2024learning,
title={Learning symmetries via weight-sharing with doubly stochastic tensors},
author={Putri A Van der Linden and Alejandro Garc{\'\i}a Castellanos and Sharvaree Vadgama and Thijs P. Kuipers and Erik J Bekkers},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=44WWOW4GPF}
} | Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods require the groups of interest to be known beforehand, which may not be realistic for real-world data. Additionally, baking in fixed group equivariance may impose overly restrictive constraints on model architecture. This highlights the need for methods that can dynamically discover and apply symmetries as soft constraints. For neural network architectures, equivariance is commonly achieved through group transformations of a canonical weight tensor, resulting in weight sharing over a given group $G$. In this work, we propose to *learn* such a weight-sharing scheme by defining a collection of learnable doubly stochastic matrices that act as soft permutation matrices on canonical weight tensors, which can take regular group representations as a special case. This yields learnable kernel transformations that are jointly optimized with downstream tasks. We show that when the dataset exhibits strong symmetries, the permutation matrices will converge to regular group representations and our weight-sharing networks effectively become regular group convolutions. Additionally, the flexibility of the method enables it to effectively pick up on partial symmetries. | Learning symmetries via weight-sharing with doubly stochastic tensors | [
"Putri A Van der Linden",
"Alejandro García Castellanos",
"Sharvaree Vadgama",
"Thijs P. Kuipers",
"Erik J Bekkers"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=444LAH3MhG | @inproceedings{
lu2024boundary,
title={Boundary Matters: A Bi-Level Active Finetuning Method},
author={Han Lu and Yichen Xie and Xiaokang Yang and Junchi Yan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=444LAH3MhG}
} | The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields. However, the finetuning phase still requires high-quality annotated samples. To overcome this challenge, the concept of active finetuning has emerged, aiming to select the most appropriate samples for model finetuning within a limited budget. Existing active learning methods struggle in this scenario due to their inherent bias in batch selection. Meanwhile, the recent active finetuning approach focuses solely on global distribution alignment but neglects the contributions of samples to local boundaries. Therefore, we propose a Bi-Level Active Finetuning framework (BiLAF) to select the samples for annotation in one shot, encompassing two stages: core sample selection for global diversity and boundary sample selection for local decision uncertainty. Without the need of ground-truth labels, our method can successfully identify pseudo-class centers, apply a novel denoising technique, and iteratively select boundary samples with designed evaluation metric. Extensive experiments provide qualitative and quantitative evidence of our method's superior efficacy, consistently outperforming the existing baselines. | Boundary Matters: A Bi-Level Active Finetuning Method | [
"Han Lu",
"Yichen Xie",
"Xiaokang Yang",
"Junchi Yan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=41lovPOCo5 | @inproceedings{
chen2024tablerag,
title={Table{RAG}: Million-Token Table Understanding with Language Models},
author={Si-An Chen and Lesly Miculicich and Julian Martin Eisenschlos and Zifeng Wang and Zilong Wang and Yanfei Chen and Yasuhisa Fujii and Hsuan-Tien Lin and Chen-Yu Lee and Tomas Pfister},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=41lovPOCo5}
} | Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables.
However, these methods often require the entire table as input, leading to scalability challenges due to the positional bias or context length constraints.
In response to these challenges, we introduce TableRAG, a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding.
TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information before providing it to the LMs.
This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss.
We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale.
Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding. | TableRAG: Million-Token Table Understanding with Language Models | [
"Si-An Chen",
"Lesly Miculicich",
"Julian Martin Eisenschlos",
"Zifeng Wang",
"Zilong Wang",
"Yanfei Chen",
"Yasuhisa Fujii",
"Hsuan-Tien Lin",
"Chen-Yu Lee",
"Tomas Pfister"
] | NeurIPS.cc/2024/Conference | 2410.04739 | [
""
] | https://huggingface.co/papers/2410.04739 | 2 | 1 | 0 | 10 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=40pE5pFhWl | @inproceedings{
cornet2024equivariant,
title={Equivariant Neural Diffusion for Molecule Generation},
author={Fran{\c{c}}ois R J Cornet and Grigory Bartosh and Mikkel N. Schmidt and Christian A. Naesseth},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=40pE5pFhWl}
} | We introduce Equivariant Neural Diffusion (END), a novel diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Compared to current state-of-the-art equivariant diffusion models, the key innovation in END lies in its learnable forward process for enhanced generative modelling. Rather than pre-specified, the forward process is parameterized through a time- and data-dependent transformation that is equivariant to rigid transformations. Through a series of experiments on standard molecule generation benchmarks, we demonstrate the competitive performance of END compared to several strong baselines for both unconditional and conditional generation. | Equivariant Neural Diffusion for Molecule Generation | [
"François R J Cornet",
"Grigory Bartosh",
"Mikkel N. Schmidt",
"Christian A. Naesseth"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3xHCaDdYcc | @inproceedings{
ni2024etoefficient,
title={{ETO}:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses},
author={Junjie Ni and Guofeng Zhang and Guanglin Li and Yijin Li and Xinyang Liu and Zhaoyang Huang and Hujun Bao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3xHCaDdYcc}
} | We tackle the efficiency problem of learning local feature matching.Recent advancements have given rise to purely CNN-based and transformer-based approaches, each augmented with deep learning techniques. While CNN-based methods often excel in matching speed, transformer-based methods tend to provide more accurate matches. We propose an efficient transformer-based network architecture for local feature matching.This technique is built on constructing multiple homography hypotheses to approximate the continuous correspondence in the real world and uni-directional cross-attention to accelerate the refinement. On the YFCC100M dataset, our matching accuracy is competitive with LoFTR, a state-of-the-art transformer-based architecture, while the inference speed is boosted to 4 times, even outperforming the CNN-based methods.Comprehensive evaluations on other open datasets such as Megadepth, ScanNet, and HPatches demonstrate our method's efficacy, highlighting its potential to significantly enhance a wide array of downstream applications. | ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses | [
"Junjie Ni",
"Guofeng Zhang",
"Guanglin Li",
"Yijin Li",
"Xinyang Liu",
"Zhaoyang Huang",
"Hujun Bao"
] | NeurIPS.cc/2024/Conference | 2410.22733 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3vJbgcjgvd | @inproceedings{
bayati2024higherorder,
title={Higher-Order Causal Message Passing for Experimentation with Complex Interference},
author={Mohsen Bayati and Yuwei Luo and William Overman and Sadegh Shirani and Ruoxuan Xiong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3vJbgcjgvd}
} | Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences and online marketplaces, where treating one experimental unit can influence outcomes for others through direct or indirect interactions. Such interference can lead to biased treatment effect estimates, particularly when the structure of these interactions is unknown. We address this challenge by introducing a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference. Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data to estimate the evolution of the system state. Concretely, we construct non-linear features from the moments of unit outcomes and treatments and then learn a function that maps these features to future mean and variance of unit outcomes. This allows for the estimation of the treatment effect over time. Extensive simulations across multiple domains, using synthetic and real network data, demonstrate the efficacy of our approach in estimating total treatment effect dynamics, even in cases where interference exhibits non-monotonic behavior in the probability of treatment. | Higher-Order Causal Message Passing for Experimentation with Complex Interference | [
"Mohsen Bayati",
"Yuwei Luo",
"William Overman",
"Sadegh Shirani",
"Ruoxuan Xiong"
] | NeurIPS.cc/2024/Conference | 2411.00945 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3vHfwL2stG | @inproceedings{
tang2024the,
title={The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space},
author={Hongyao Tang and Min Zhang and Chen Chen and Jianye HAO},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3vHfwL2stG}
} | Knowing the learning dynamics of policy is significant to unveiling the mysteries of Reinforcement Learning (RL). It is especially crucial yet challenging to Deep RL, from which the remedies to notorious issues like sample inefficiency and learning instability could be obtained. In this paper, we study how the policy networks of typical DRL agents evolve during the learning process by empirically investigating several kinds of temporal change for each policy parameter. In popular MuJoCo and DeepMind Control Suite (DMC) environments, we find common phenomena for TD3 and RAD agents: (1) the activity of policy network parameters is highly asymmetric and policy networks advance monotonically along a very limited number of major parameter directions; (2) severe detours occur in parameter update and harmonic-like changes are observed for all minor parameter directions. By performing a novel temporal SVD along the policy learning path, the major and minor parameter directions are identified as the columns of the right unitary matrix associated with dominant and insignificant singular values respectively. Driven by the discoveries above, we propose a simple and effective method, called Policy Path Trimming and Boosting (PPTB), as a general plug-in improvement to DRL algorithms. The key idea of PPTB is to trim the policy learning path by canceling the policy updates in minor parameter directions, and boost the learning path by encouraging the advance in major directions. In experiments, we demonstrate that our method improves the learning performance of TD3, RAD, and DoubleDQN regarding scores and efficiency in MuJoCo, DMC, and MinAtar tasks respectively. | The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space | [
"Hongyao Tang",
"Min Zhang",
"Chen Chen",
"Jianye HAO"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3uUIwMxYbR | @inproceedings{
ding2024revisiting,
title={Revisiting Differentially Private Re{LU} Regression},
author={Meng Ding and Mingxi Lei and Liyang Zhu and Shaowei Wang and Di Wang and Jinhui Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3uUIwMxYbR}
} | As one of the most fundamental non-convex learning problems, ReLU regression under differential privacy (DP) constraints, especially in high-dimensional settings, remains a challenging area in privacy-preserving machine learning. Existing results are limited to the assumptions of bounded norm $ \|\mathbf{x}\|_2 \leq 1$, which becomes meaningless with increasing data dimensionality. In this work, we revisit the problem of DP ReLU regression in high-dimensional regimes. We propose two innovative algorithms DP-GLMtron and DP-TAGLMtron that outperform the conventional DPSGD.
DP-GLMtron is based on a generalized linear model perceptron approach, integrating adaptive clipping and Gaussian mechanism for enhanced privacy. To overcome the constraints of small privacy budgets in DP-GLMtron, represented by $\widetilde{O}(\sqrt{1/N})$ where $N$ is the sample size, we introduce DP-TAGLMtron, which utilizes a tree aggregation protocol to balance privacy and utility effectively, showing that DP-TAGLMtron achieves comparable performance with only an additional factor of $O(\log N)$ in the utility upper bound.
Moreover, our theoretical analysis extends beyond Gaussian-like data distributions to settings with eigenvalue decay, showing how data distribution impacts learning in high dimensions. Notably, our findings suggest that the utility upper bound could be independent of the dimension $d$, even when $d \gg N$.
Experiments on synthetic and real-world datasets also validate our results. | Revisiting Differentially Private ReLU Regression | [
"Meng Ding",
"Mingxi Lei",
"Liyang Zhu",
"Shaowei Wang",
"Di Wang",
"Jinhui Xu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3uQtNWNTwz | @inproceedings{
sobol2024zerotohero,
title={Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering},
author={Ido Sobol and Chenfeng Xu and Or Litany},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3uQtNWNTwz}
} | Generating realistic images from arbitrary views based on a single source image remains a significant challenge in computer vision, with broad applications ranging from e-commerce to immersive virtual experiences. Recent advancements in diffusion models, particularly the Zero-1-to-3 model, have been widely adopted for generating plausible views, videos, and 3D models. However, these models still struggle with inconsistencies and implausibility in new views generation, especially for challenging changes in viewpoint. In this work, we propose Zero-to-Hero, a novel test-time approach that enhances view synthesis by manipulating attention maps during the denoising process of Zero-1-to-3. By drawing an analogy between the denoising process and stochastic gradient descent (SGD), we implement a filtering mechanism that aggregates attention maps, enhancing generation reliability and authenticity. This process improves geometric consistency without requiring retraining or significant computational resources. Additionally, we modify the self-attention mechanism to integrate information from the source view, reducing shape distortions. These processes are further supported by a specialized sampling schedule. Experimental results demonstrate substantial improvements in fidelity and consistency, validated on a diverse set of out-of-distribution objects. Additionally, we demonstrate the general applicability and effectiveness of Zero-to-Hero in multi-view, and image generation conditioned on semantic maps and pose. | Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering | [
"Ido Sobol",
"Chenfeng Xu",
"Or Litany"
] | NeurIPS.cc/2024/Conference | 2405.18677 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3uI4ceR4iz | @inproceedings{
yang2024sadip,
title={{SA}3{DIP}: Segment Any 3D Instance with Potential 3D Priors},
author={Xi Yang and Xu Gu and Xingyilang Yin and Xinbo Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3uI4ceR4iz}
} | The proliferation of 2D foundation models has sparked research into adapting them for open-world 3D instance segmentation. Recent methods introduce a paradigm that leverages superpoints as geometric primitives and incorporates 2D multi-view masks from Segment Anything model (SAM) as merging guidance, achieving outstanding zero-shot instance segmentation results. However, the limited use of 3D priors restricts the segmentation performance. Previous methods calculate the 3D superpoints solely based on estimated normal from spatial coordinates, resulting in under-segmentation for instances with similar geometry. Besides, the heavy reliance on SAM and hand-crafted algorithms in 2D space suffers from over-segmentation due to SAM's inherent part-level segmentation tendency. To address these issues, we propose SA3DIP, a novel method for Segmenting Any 3D Instances via exploiting potential 3D Priors. Specifically, on one hand, we generate complementary 3D primitives based on both geometric and textural priors, which reduces the initial errors that accumulate in subsequent procedures. On the other hand, we introduce supplemental constraints from the 3D space by using a 3D detector to guide a further merging process. Furthermore, we notice a considerable portion of low-quality ground truth annotations in ScanNetV2 benchmark, which affect the fair evaluations. Thus, we present ScanNetV2-INS with complete ground truth labels and supplement additional instances for 3D class-agnostic instance segmentation. Experimental evaluations on various 2D-3D datasets demonstrate the effectiveness and robustness of our approach. Our code and proposed ScanNetV2-INS dataset are available HERE. | SA3DIP: Segment Any 3D Instance with Potential 3D Priors | [
"Xi Yang",
"Xu Gu",
"Xingyilang Yin",
"Xinbo Gao"
] | NeurIPS.cc/2024/Conference | 2411.03819 | [
"https://github.com/ryang41/sa3dip"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=3uDEmsf3Jf | @inproceedings{
yao2024oasis,
title={{OASIS}: Conditional Distribution Shaping for Offline Safe Reinforcement Learning},
author={Yihang Yao and Zhepeng Cen and Wenhao Ding and Haohong Lin and Shiqi Liu and Tingnan Zhang and Wenhao Yu and Ding Zhao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3uDEmsf3Jf}
} | Offline safe reinforcement learning (RL) aims to train a policy that satisfies con- straints using a pre-collected dataset. Most current methods struggle with the mismatch between imperfect demonstrations and the desired safe and rewarding performance. In this paper, we mitigate this issue from a data-centric perspective and introduce OASIS (cOnditionAl diStributIon Shaping), a new paradigm in offline safe RL designed to overcome these critical limitations. OASIS utilizes a conditional diffusion model to synthesize offline datasets, thus shaping the data dis- tribution toward a beneficial target domain. Our approach makes compliance with safety constraints through effective data utilization and regularization techniques to benefit offline safe RL training. Comprehensive evaluations on public benchmarks and varying datasets showcase OASIS’s superiority in benefiting offline safe RL agents to achieve high-reward behavior while satisfying the safety constraints, out- performing established baselines. Furthermore, OASIS exhibits high data efficiency and robustness, making it suitable for real-world applications, particularly in tasks where safety is imperative and high-quality demonstrations are scarce. More details are available at the website https://sites.google.com/view/saferl-oasis/home. | OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning | [
"Yihang Yao",
"Zhepeng Cen",
"Wenhao Ding",
"Haohong Lin",
"Shiqi Liu",
"Tingnan Zhang",
"Wenhao Yu",
"Ding Zhao"
] | NeurIPS.cc/2024/Conference | 2407.14653 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3tj3A26wsV | @inproceedings{
yan2024a,
title={A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning},
author={Tom Yan and Zachary Chase Lipton},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3tj3A26wsV}
} | A key source of complexity in next-generation AI models is the size of model outputs, making it time-consuming to parse and provide reliable feedback on. To ensure such models are aligned, we will need to bolster our understanding of scalable oversight and how to scale up human feedback. To this end, we study the challenges of scalable oversight in the context of goal-conditioned hierarchical reinforcement learning. Hierarchical structure is a promising entrypoint into studying how to scale up human feedback, which in this work we assume can only be provided for model outputs below a threshold size. In the cardinal feedback setting, we develop an apt sub-MDP reward and algorithm that allows us to acquire and scale up low-level feedback for learning with sublinear regret. In the ordinal feedback setting, we show the necessity of both high- and low-level feedback, and develop a hierarchical experimental design algorithm that efficiently acquires both types of feedback for learning. Altogether, our work aims to consolidate the foundations of scalable oversight, formalizing and studying the various challenges thereof. | A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning | [
"Tom Yan",
"Zachary Chase Lipton"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3s8V8QP9XV | @inproceedings{
amsel2024nearly,
title={Nearly Optimal Approximation of Matrix Functions by the Lanczos Method},
author={Noah Amsel and Tyler Chen and Anne Greenbaum and Cameron N Musco and Christopher Musco},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3s8V8QP9XV}
} | Approximating the action of a matrix function $f(\vec{A})$ on a vector $\vec{b}$ is an increasingly important primitive in machine learning, data science, and statistics, with applications such as sampling high dimensional Gaussians, Gaussian process regression and Bayesian inference, principle component analysis, and approximating Hessian spectral densities.
Over the past decade, a number of algorithms enjoying strong theoretical guarantees have been proposed for this task.
Many of the most successful belong to a family of algorithms called Krylov subspace methods.
Remarkably, a classic Krylov subspace method, called the Lanczos method for matrix functions (Lanczos-FA), frequently outperforms newer methods in practice. Our main result is a theoretical justification for this finding: we show that, for a natural class of rational functions, Lanczos-FA matches the error of the best possible Krylov subspace method up to a multiplicative approximation factor.
The approximation factor depends on the degree of $f(x)$'s denominator and the condition number of $\vec{A}$, but not on the number of iterations $k$. Our result provides a strong justification for the excellent performance of Lanczos-FA, especially on functions that are well approximated by rationals, such as the matrix square root. | Nearly Optimal Approximation of Matrix Functions by the Lanczos Method | [
"Noah Amsel",
"Tyler Chen",
"Anne Greenbaum",
"Cameron N Musco",
"Christopher Musco"
] | NeurIPS.cc/2024/Conference | 2303.03358 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=3qUks3wrnH | @inproceedings{
he2024efficient,
title={Efficient Multi-task Reinforcement Learning with Cross-Task Policy Guidance},
author={Jinmin He and Kai Li and Yifan Zang and Haobo Fu and QIANG FU and Junliang Xing and Jian Cheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3qUks3wrnH}
} | Multi-task reinforcement learning endeavors to efficiently leverage shared information across various tasks, facilitating the simultaneous learning of multiple tasks. Existing approaches primarily focus on parameter sharing with carefully designed network structures or tailored optimization procedures. However, they overlook a direct and complementary way to exploit cross-task similarities: the control policies of tasks already proficient in some skills can provide explicit guidance for unmastered tasks to accelerate skills acquisition. To this end, we present a novel framework called Cross-Task Policy Guidance (CTPG), which trains a guide policy for each task to select the behavior policy interacting with the environment from all tasks' control policies, generating better training trajectories. In addition, we propose two gating mechanisms to improve the learning efficiency of CTPG: one gate filters out control policies that are not beneficial for guidance, while the other gate blocks tasks that do not necessitate guidance. CTPG is a general framework adaptable to existing parameter sharing approaches. Empirical evaluations demonstrate that incorporating CTPG with these approaches significantly enhances performance in manipulation and locomotion benchmarks. | Efficient Multi-task Reinforcement Learning with Cross-Task Policy Guidance | [
"Jinmin He",
"Kai Li",
"Yifan Zang",
"Haobo Fu",
"QIANG FU",
"Junliang Xing",
"Jian Cheng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3pWHKxK1sC | @inproceedings{
zhou2024conformal,
title={Conformal Classification with Equalized Coverage for Adaptively Selected Groups},
author={Yanfei Zhou and Matteo Sesia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3pWHKxK1sC}
} | This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen features. These features are carefully selected to reflect potential model limitations or biases. This can be useful to find a practical compromise between efficiency---by providing informative predictions---and algorithmic fairness---by ensuring equalized coverage for the most sensitive groups. We demonstrate the validity and effectiveness of this method on simulated and real data sets. | Conformal Classification with Equalized Coverage for Adaptively Selected Groups | [
"Yanfei Zhou",
"Matteo Sesia"
] | NeurIPS.cc/2024/Conference | 2405.15106 | [
"https://github.com/fionaz3696/adaptively-fair-conformal-prediction"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3og0FT85B2 | @inproceedings{
waczynska2024dmiso,
title={D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup},
author={Joanna Waczynska and Piotr Borycki and Joanna Kaleta and Slawomir Tadeja and Przemys{\l}aw Spurek},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3og0FT85B2}
} | Over the past years, we have observed an abundance of approaches for modeling dynamic 3D scenes using Gaussian Splatting (GS). These solutions use GS to represent the scene's structure and the neural network to model dynamics. Such approaches allow fast rendering and extracting each element of such a dynamic scene. However, modifying such objects over time is challenging. SC-GS (Sparse Controlled Gaussian Splatting) enhanced with Deformed Control Points partially solves this issue. However, this approach necessitates selecting elements that need to be kept fixed, as well as centroids that should be adjusted throughout editing. Moreover, this task poses additional difficulties regarding the re-productivity of such editing. To address this, we propose Dynamic Multi-Gaussian Soup (D-MiSo), which allows us to model the mesh-inspired representation of dynamic GS. Additionally, we propose a strategy of linking parameterized Gaussian splats, forming a Triangle Soup with the estimated mesh. Consequently, we can separately construct new trajectories for the 3D objects composing the scene. Thus, we can make the scene's dynamic editable over time or while maintaining partial dynamics. | D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup | [
"Joanna Waczynska",
"Piotr Borycki",
"Joanna Kaleta",
"Slawomir Tadeja",
"Przemysław Spurek"
] | NeurIPS.cc/2024/Conference | 2405.14276 | [
"https://github.com/waczjoan/D-MiSo"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3mCr7ZNdSw | @inproceedings{
greenewald2024privacy,
title={Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training},
author={Kristjan Greenewald and Yuancheng Yu and Hao Wang and Kai Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3mCr7ZNdSw}
} | Training generative models with differential privacy (DP) typically involves injecting noise into gradient updates or adapting the discriminator's training procedure. As a result, such approaches often struggle with hyper-parameter tuning and convergence.
We consider the \emph{slicing privacy mechanism} that injects noise into random low-dimensional projections of the private data, and provide strong privacy guarantees for it. These noisy projections are used for training generative models.
To enable optimizing generative models using this DP approach, we introduce the \emph{smoothed-sliced $f$-divergence} and show it enjoys statistical consistency.
Moreover, we present a kernel-based estimator for this divergence, circumventing the need for adversarial training.
Extensive numerical experiments demonstrate that our approach can generate synthetic data of higher quality compared with baselines. Beyond performance improvement, our method, by sidestepping the need for noisy gradients, offers data scientists the flexibility to adjust generator architecture and hyper-parameters, run the optimization over any number of epochs, and even restart the optimization process---all without incurring additional privacy costs. | Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training | [
"Kristjan Greenewald",
"Yuancheng Yu",
"Hao Wang",
"Kai Xu"
] | NeurIPS.cc/2024/Conference | 2410.19941 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3m5ndUNQYt | @inproceedings{
yang2024diffusionbased,
title={Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection},
author={Ying Yang and De Cheng and Chaowei Fang and Yubiao Wang and Changzhe Jiao and Lechao Cheng and Nannan Wang and Xinbo Gao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3m5ndUNQYt}
} | Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for developing a safe real-world machine learning system. Current reconstruction-based method provides a good alternative approach, by measuring the reconstruction error between the input and its corresponding generative counterpart in the pixel/feature space. However, such generative methods face the key dilemma, $i.e.$, improving the reconstruction power of the generative model, while keeping compact representation of the ID data. To address this issue, we propose the diffusion-based layer-wise semantic reconstruction approach for unsupervised OOD detection. The innovation of our approach is that we leverage the diffusion model's intrinsic data reconstruction ability to distinguish ID samples from OOD samples in the latent feature space. Moreover, to set up a comprehensive and discriminative feature representation, we devise a multi-layer semantic feature extraction strategy. Through distorting the extracted features with Gaussian noises and applying the diffusion model for feature reconstruction, the separation of ID and OOD samples is implemented according to the reconstruction errors. Extensive experimental results on multiple benchmarks built upon various datasets demonstrate that our method achieves state-of-the-art performance in terms of detection accuracy and speed. | Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection | [
"Ying Yang",
"De Cheng",
"Chaowei Fang",
"Yubiao Wang",
"Changzhe Jiao",
"Lechao Cheng",
"Nannan Wang",
"Xinbo Gao"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/xbyym/dlsr"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3lic0JgPRZ | @inproceedings{
huang2024learning,
title={Learning to Decouple the Lights for 3D Face Texture Modeling},
author={Tianxin Huang and Zhenyu Zhang and Ying Tai and Gim Hee Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3lic0JgPRZ}
} | Existing research has made impressive strides in reconstructing human facial shapes and textures from images with well-illuminated faces and minimal external occlusions.
Nevertheless, it remains challenging to recover accurate facial textures from scenarios with complicated illumination affected by external occlusions, \eg a face that is partially obscured by items such as a hat.
Existing works based on the assumption of single and uniform illumination cannot correctly process these data.
In this work, we introduce a novel approach to model 3D facial textures under such unnatural illumination. Instead of assuming single illumination, our framework learns to imitate the unnatural illumination as a composition of multiple separate light conditions combined with learned neural representations, named Light Decoupling.
According to experiments on both single images and video sequences, we demonstrate the effectiveness of our approach in modeling facial textures under challenging illumination affected by occlusions. | Learning to Decouple the Lights for 3D Face Texture Modeling | [
"Tianxin Huang",
"Zhenyu Zhang",
"Ying Tai",
"Gim Hee Lee"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3lQgEPRxeu | @inproceedings{
bai2024learning,
title={Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained {MDP}s via Primal-Dual Policy Gradient Algorithm},
author={Qinbo Bai and Washim Uddin Mondal and Vaneet Aggarwal},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3lQgEPRxeu}
} | This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDPs). To the best of our knowledge, this work is the first to delve into the regret and constraint violation analysis of average reward CMDPs with a general policy parametrization. To address this challenge, we propose a primal dual-based policy gradient algorithm that adeptly manages the constraints while ensuring a low regret guarantee toward achieving a global optimal policy. In particular, our proposed algorithm achieves $\tilde{\mathcal{O}}({T}^{4/5})$ objective regret and $\tilde{\mathcal{O}}({T}^{4/5})$ constraint violation bounds. | Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm | [
"Qinbo Bai",
"Washim Uddin Mondal",
"Vaneet Aggarwal"
] | NeurIPS.cc/2024/Conference | 2402.02042 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3l2HnZXNou | @inproceedings{
ding2024multiagent,
title={Multi-Agent Coordination via Multi-Level Communication},
author={Ziluo Ding and Zeyuan Liu and Zhirui Fang and Kefan Su and Liwen Zhu and Zongqing Lu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3l2HnZXNou}
} | The partial observability and stochasticity in multi-agent settings can be mitigated by accessing more information about others via communication. However, the coordination problem still exists since agents cannot communicate actual actions with each other at the same time due to the circular dependencies. In this paper, we propose a novel multi-level communication scheme, Sequential Communication (SeqComm). SeqComm treats agents asynchronously (the upper-level agents make decisions before the lower-level ones) and has two communication phases. In the negotiation phase, agents determine the priority of decision-making by communicating hidden states of observations and comparing the value of intention, which is obtained by modeling the environment dynamics. In the launching phase, the upper-level agents take the lead in making decisions and then communicate their actions with the lower-level agents. Theoretically, we prove the policies learned by SeqComm are guaranteed to improve monotonically and converge. Empirically, we show that SeqComm outperforms existing methods in a variety of cooperative multi-agent tasks. | Multi-Agent Coordination via Multi-Level Communication | [
"Ziluo Ding",
"Zeyuan Liu",
"Zhirui Fang",
"Kefan Su",
"Liwen Zhu",
"Zongqing Lu"
] | NeurIPS.cc/2024/Conference | 2209.12713 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3kDWoqs2X2 | @inproceedings{
so2024fearless,
title={Fearless Stochasticity in Expectation Propagation},
author={Jonathan So and Richard E. Turner},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3kDWoqs2X2}
} | Expectation propagation (EP) is a family of algorithms for performing approximate inference in probabilistic models. The updates of EP involve the evaluation of moments—expectations of certain functions—which can be estimated from Monte Carlo (MC) samples. However, the updates are not robust to MC noise when performed naively, and various prior works have attempted to address this issue in different ways. In this work, we provide a novel perspective on the moment-matching updates of EP; namely, that they perform natural-gradient-based optimisation of a variational objective. We use this insight to motivate two new EP variants, with updates that are particularly well-suited to MC estimation. They remain stable and are most sample-efficient when estimated with just a single sample. These new variants combine the benefits of their predecessors and address key weaknesses. In particular, they are easier to tune, offer an improved speed-accuracy trade-off, and do not rely on the use of debiasing estimators. We demonstrate their efficacy on a variety of probabilistic inference tasks. | Fearless Stochasticity in Expectation Propagation | [
"Jonathan So",
"Richard E. Turner"
] | NeurIPS.cc/2024/Conference | 2406.01801 | [
"https://github.com/cambridge-mlg/fearless-ep"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=3j2nasmKkP | @inproceedings{
huang2024clusterwise,
title={Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention},
author={Siyuan Huang and Yunchong Song and Jiayue Zhou and Zhouhan Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3j2nasmKkP}
} | In the realm of graph learning, there is a category of methods that conceptualize graphs as hierarchical structures, utilizing node clustering to capture broader structural information. While generally effective, these methods often rely on a fixed graph coarsening routine, leading to overly homogeneous cluster representations and loss of node-level information. In this paper, we envision the graph as a network of interconnected node sets without compressing each cluster into a single embedding. To enable effective information transfer among these node sets, we propose the Node-to-Cluster Attention (N2C-Attn) mechanism. N2C-Attn incorporates techniques from Multiple Kernel Learning into the kernelized attention framework, effectively capturing information at both node and cluster levels. We then devise an efficient form for N2C-Attn using the cluster-wise message-passing framework, achieving linear time complexity. We further analyze how N2C-Attn combines bi-level feature maps of queries and keys, demonstrating its capability to merge dual-granularity information. The resulting architecture, Cluster-wise Graph Transformer (Cluster-GT), which uses node clusters as tokens and employs our proposed N2C-Attn module, shows superior performance on various graph-level tasks. Code is available at https://github.com/LUMIA-Group/Cluster-wise-Graph-Transformer. | Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention | [
"Siyuan Huang",
"Yunchong Song",
"Jiayue Zhou",
"Zhouhan Lin"
] | NeurIPS.cc/2024/Conference | 2410.06746 | [
"https://github.com/lumia-group/cluster-wise-graph-transformer"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=3ivnixHy16 | @inproceedings{
wu2024boosting,
title={Boosting Text-to-Video Generative Model with {MLLM}s Feedback},
author={Xun Wu and Shaohan Huang and Guolong Wang and Jing Xiong and Furu Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3ivnixHy16}
} | Recent advancements in text-to-video generative models, such as Sora, have showcased impressive capabilities. These models have attracted significant interest for their potential applications. However, they often rely on extensive datasets of variable quality, which can result in generated videos that lack aesthetic appeal and do not accurately reflect the input text prompts. A promising approach to mitigate these issues is to leverage Reinforcement Learning from Human Feedback (RLHF), which aims to align the outputs of text-to-video generative with human preferences. However, the considerable costs associated with manual annotation have led to a scarcity of comprehensive preference datasets. In response to this challenge, our study begins by investigating the efficacy of Multimodal Large Language Models (MLLMs) generated annotations in capturing video preferences, discovering a high degree of concordance with human judgments. Building upon this finding, we utilize MLLMs to perform fine-grained video preference annotations across two dimensions, resulting in the creation of VideoPrefer, which includes 135,000 preference annotations. Utilizing this dataset, we introduce VideoRM, the first general-purpose reward model tailored for video preference in the text-to-video domain. Our comprehensive experiments confirm the effectiveness of both VideoPrefer and VideoRM, representing a significant step forward in the field. | Boosting Text-to-Video Generative Model with MLLMs Feedback | [
"Xun Wu",
"Shaohan Huang",
"Guolong Wang",
"Jing Xiong",
"Furu Wei"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3ilqQHBWTf | @inproceedings{
chen2024laseev,
title={LaSe-E2V: Towards Language-guided Semantic-aware Event-to-Video Reconstruction},
author={Kanghao Chen and Hangyu Li and Jiazhou Zhou and Zeyu Wang and Lin Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3ilqQHBWTf}
} | Event cameras harness advantages such as low latency, high temporal resolution, and high dynamic range (HDR), compared to standard cameras. Due to the distinct imaging paradigm shift, a dominant line of research focuses on event-to-video (E2V) reconstruction to bridge event-based and standard computer vision. However, this task remains challenging due to its inherently ill-posed nature: event cameras only detect the edge and motion information locally. Consequently, the reconstructed videos are often plagued by artifacts and regional blur, primarily caused by the ambiguous semantics of event data. In this paper, we find language naturally conveys abundant semantic information, rendering it stunningly superior in ensuring semantic consistency for E2V reconstruction. Accordingly, we propose a novel framework, called LaSe-E2V, that can achieve semantic-aware high-quality E2V reconstruction from a language-guided perspective, buttressed by the text-conditional diffusion models. However, due to diffusion models' inherent diversity and randomness, it is hardly possible to directly apply them to achieve spatial and temporal consistency for E2V reconstruction. Thus, we first propose an Event-guided Spatiotemporal Attention (ESA) module to condition the event data to the denoising pipeline effectively. We then introduce an event-aware mask loss to ensure temporal coherence and a noise initialization strategy to enhance spatial consistency. Given the absence of event-text-video paired data, we aggregate existing E2V datasets and generate textual descriptions using the tagging models for training and evaluation. Extensive experiments on three datasets covering diverse challenging scenarios (e.g., fast motion, low light) demonstrate the superiority of our method. Demo videos for the results are attached to the project page. | LaSe-E2V: Towards Language-guided Semantic-aware Event-to-Video Reconstruction | [
"Kanghao Chen",
"Hangyu Li",
"Jiazhou Zhou",
"Zeyu Wang",
"Lin Wang"
] | NeurIPS.cc/2024/Conference | 2407.05547 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3ie8NWA1El | @inproceedings{
du2024hyperprism,
title={HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-{IID} Data and Time-varying Communication Links},
author={Haizhou Du and Yijian Chen and Ryan Yang and Yuchen Li and Linghe Kong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3ie8NWA1El}
} | While Distributed Machine Learning (DML) has been widely used to achieve decent performance, it is still challenging to take full advantage of data and devices distributed at multiple vantage points to adapt and learn, especially it is non-trivial to address dynamic and divergence challenges based on the linear aggregation framework as follows: (1) heterogeneous learning data at different devices (i.e., non-IID data) resulting in model divergence and (2) in the case of time-varying communication links, the limited ability for devices to reconcile model divergence. In this paper, we contribute a non-linear class aggregation framework HyperPrism that leverages distributed mirror descent with averaging done in the mirror descent dual space and adapts the degree of Weighted Power Mean (WPM) used in each round. Moreover, HyperPrism could adaptively choose different mapping for different layers of the local model with a dedicated hypernetwork per device, achieving automatic optimization of DML in high divergence settings. We perform rigorous analysis and experimental evaluations to demonstrate the effectiveness of adaptive, mirror-mapping DML. In particular, we extend the generalizability of existing related works and position them as special cases within HyperPrism. Our experimental results show that HyperPrism can improve the convergence speed up to 98.63% and scale well to more devices compared with the state-of-the-art, all with little additional computation overhead compared to traditional linear aggregation. | HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links | [
"Haizhou Du",
"Yijian Chen",
"Ryan Yang",
"Yuchen Li",
"Linghe Kong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3iOefhez5e | @inproceedings{
huang2024low,
title={Low Degree Hardness for Broadcasting on Trees},
author={Han Huang and Elchanan Mossel},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3iOefhez5e}
} | We study the low-degree hardness of broadcasting on trees.
Broadcasting on trees has been extensively studied in statistical physics,
in computational biology in relation to phylogenetic reconstruction and in statistics and computer science in the context of block model inference, and as a simple data model for algorithms that may require depth for inference.
The inference of the root can be carried by celebrated Belief Propagation (BP) algorithm which achieves Bayes-optimal performance. Despite the fact that this algorithm runs in linear time (using real operations), recent works indicated that this algorithm in fact requires high level of complexity.
Moitra, Mossel and Sandon constructed a chain for which estimating the root better than random (for a typical input) is $NC1$ complete. Kohler and Mossel constructed chains such that for trees with $N$ leaves, recovering the root better than random requires a polynomial of degree $N^{\Omega(1)}$. Both works above asked if such complexity bounds hold in general below the celebrated {\em Kesten-Stigum} bound.
In this work, we prove that this is indeed the case for low degree polynomials.
We show that for the broadcast problem using any Markov chain on trees with $N$ leaves, below the Kesten Stigum bound, any $O(\log N)$ degree polynomial has vanishing correlation with the root.
Our result is one of the first low-degree lower bound that is proved in a setting that is not based or easily reduced to a product measure. | Low Degree Hardness for Broadcasting on Trees | [
"Han Huang",
"Elchanan Mossel"
] | NeurIPS.cc/2024/Conference | 2402.13359 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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