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null | https://openreview.net/forum?id=JEflV4nRlH | @inproceedings{
jain2024what,
title={What Makes and Breaks Safety Fine-tuning? A Mechanistic Study},
author={Samyak Jain and Ekdeep Singh Lubana and Kemal Oksuz and Tom Joy and Philip Torr and Amartya Sanyal and Puneet K. Dokania},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JEflV4nRlH}
} | Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning, we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed upon (e.g., a “cycle” vs. a “bomb”). Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning, direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights’ null space. This yields a clustering of inputs based on whether the model deems them safe or not. Correspondingly, when an adversarial input (e.g., a jailbreak) is provided, its activations are closer to safer samples, leading to the model processing such an input as if it were safe. Code is available at https://github.com/fiveai/understanding_safety_finetuning. | What Makes and Breaks Safety Fine-tuning? A Mechanistic Study | [
"Samyak Jain",
"Ekdeep Singh Lubana",
"Kemal Oksuz",
"Tom Joy",
"Philip Torr",
"Amartya Sanyal",
"Puneet K. Dokania"
] | NeurIPS.cc/2024/Conference | 2407.10264 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JEKXTLjEIq | @inproceedings{
dinitz2024binary,
title={Binary Search with Distributional Predictions},
author={Michael Dinitz and Sungjin Im and Thomas Lavastida and Benjamin Moseley and Aidin Niaparast and Sergei Vassilvitskii},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JEKXTLjEIq}
} | Algorithms with (machine-learned) predictions is a powerful framework for combining traditional worst-case algorithms with modern machine learning. However, the vast majority of work in this space assumes that the prediction itself is non-probabilistic, even if it is generated by some stochastic process (such as a machine learning system). This is a poor fit for modern ML, particularly modern neural networks, which naturally generate a *distribution*. We initiate the study of algorithms with *distributional* predictions, where the prediction itself is a distribution. We focus on one of the simplest yet fundamental settings: binary search (or searching a sorted array).
This setting has one of the simplest algorithms with a point prediction, but what happens if the prediction is a distribution? We show that this is a richer setting: there are simple distributions where using the classical prediction-based algorithm with any single prediction does poorly.
Motivated by this, as our main result, we give an algorithm with query complexity
$O(H(p) + \log \eta)$, where $H(p)$ is the entropy of the true distribution $p$ and $\eta$ is the earth mover's distance between $p$ and the predicted distribution $\hat p$. This also yields the first *distributionally-robust* algorithm for the classical problem of computing an optimal binary search tree given a distribution over target keys.
We complement this with a lower bound showing that this query complexity is essentially optimal (up to constants), and experiments validating the practical usefulness of our algorithm. | Binary Search with Distributional Predictions | [
"Michael Dinitz",
"Sungjin Im",
"Thomas Lavastida",
"Benjamin Moseley",
"Aidin Niaparast",
"Sergei Vassilvitskii"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JDAQwysFOc | @inproceedings{
wang2024nonconvolutional,
title={Non-convolutional graph neural networks.},
author={Yuanqing Wang and Kyunghyun Cho},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JDAQwysFOc}
} | Rethink convolution-based graph neural networks (GNN)---they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation.
Here, we design a simple graph learning module entirely free of convolution operators, coined _random walk with unifying memory_ (RUM) neural network, where an RNN merges the topological and semantic graph features along the random walks terminating at each node.
Relating the rich literature on RNN behavior and graph topology, we theoretically show and experimentally verify that RUM attenuates the aforementioned symptoms and is more expressive than the Weisfeiler-Lehman (WL) isomorphism test.
On a variety of node- and graph-level classification and regression tasks, RUM not only achieves competitive performance, but is also robust, memory-efficient, scalable, and faster than the simplest convolutional GNNs. | Non-convolutional graph neural networks. | [
"Yuanqing Wang",
"Kyunghyun Cho"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/yuanqing-wang/rum"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=JD3NYpeQ3R | @inproceedings{
cherian2024large,
title={Large language model validity via enhanced conformal prediction methods},
author={John Cherian and Isaac Gibbs and Emmanuel Candes},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JD3NYpeQ3R}
} | We develop new conformal inference methods for obtaining validity guarantees on the output of large language models (LLMs). Prior work in conformal language modeling identifies a subset of the text that satisfies a high-probability guarantee of correctness. These methods work by filtering claims from the LLM's original response if a scoring function evaluated on the claim fails to exceed a threshold calibrated via split conformal prediction. Existing methods in this area suffer from two deficiencies. First, the guarantee stated is not conditionally valid. The trustworthiness of the filtering step may vary based on the topic of the response. Second, because the scoring function is imperfect, the filtering step can remove many valuable and accurate claims. We address both of these challenges via two new conformal methods. First, we generalize the conditional conformal procedure of Gibbs et al. (2023) in order to adaptively issue weaker guarantees when they are required to preserve the utility of the output. Second, we show how to systematically improve the quality of the scoring function via a novel algorithm for differentiating through the conditional conformal procedure. We demonstrate the efficacy of our approach on biography and medical question-answering datasets. | Large language model validity via enhanced conformal prediction methods | [
"John Cherian",
"Isaac Gibbs",
"Emmanuel Candes"
] | NeurIPS.cc/2024/Conference | 2406.09714 | [
"https://github.com/jjcherian/conformal-safety"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JCyBN5syv3 | @inproceedings{
zhou2024simgen,
title={SimGen: Simulator-conditioned Driving Scene Generation},
author={Yunsong Zhou and Michael Simon and Zhenghao Peng and Sicheng Mo and Hongzi Zhu and Minyi Guo and Bolei Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JCyBN5syv3}
} | Controllable synthetic data generation can substantially lower the annotation cost of training data. Prior works use diffusion models to generate driving images conditioned on the 3D object layout. However, those models are trained on small-scale datasets like nuScenes, which lack appearance and layout diversity. Moreover, overfitting often happens, where the trained models can only generate images based on the layout data from the validation set of the same dataset. In this work, we introduce a simulator-conditioned scene generation framework called SimGen that can learn to generate diverse driving scenes by mixing data from the simulator and the real world. It uses a novel cascade diffusion pipeline to address challenging sim-to-real gaps and multi-condition conflicts. A driving video dataset DIVA is collected to enhance the generative diversity of SimGen, which contains over 147.5 hours of real-world driving videos from 73 locations worldwide and simulated driving data from the MetaDrive simulator. SimGen achieves superior generation quality and diversity while preserving controllability based on the text prompt and the layout pulled from a simulator. We further demonstrate the improvements brought by SimGen for synthetic data augmentation on the BEV detection and segmentation task and showcase its capability in safety-critical data generation. | SimGen: Simulator-conditioned Driving Scene Generation | [
"Yunsong Zhou",
"Michael Simon",
"Zhenghao Peng",
"Sicheng Mo",
"Hongzi Zhu",
"Minyi Guo",
"Bolei Zhou"
] | NeurIPS.cc/2024/Conference | 2406.09386 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=JC1VKK3UXk | @inproceedings{
herde2024poseidon,
title={Poseidon: Efficient Foundation Models for {PDE}s},
author={Maximilian Herde and Bogdan Raonic and Tobias Rohner and Roger K{\"a}ppeli and Roberto Molinaro and Emmanuel de Bezenac and Siddhartha Mishra},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JC1VKK3UXk}
} | We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enable continuous-in-time evaluations. A novel training strategy leveraging the semi-group property of time-dependent PDEs to allow for significant scaling-up of the training data is also proposed. Poseidon is pretrained on a diverse, large scale dataset for the governing equations of fluid dynamics. It is then evaluated on a suite of 15 challenging downstream tasks that include a wide variety of PDE types and operators. We show that Poseidon exhibits excellent performance across the board by outperforming baselines significantly, both in terms of sample efficiency and accuracy. Poseidon also generalizes very well to new physics that is not seen during pretraining. Moreover, Poseidon scales with respect to model and data size, both for pretraining and for downstream tasks. Taken together, our results showcase the surprising ability of Poseidon to learn effective representations from a very small set of PDEs during pretraining in order to generalize well to unseen and unrelated PDEs downstream, demonstrating its potential as an effective, general purpose PDE foundation model. Finally, the Poseidon model as well as underlying pretraining and downstream datasets are open sourced, with code being available at https://github.com/camlab-ethz/poseidon and pretrained models and datasets at https://huggingface.co/camlab-ethz. | Poseidon: Efficient Foundation Models for PDEs | [
"Maximilian Herde",
"Bogdan Raonic",
"Tobias Rohner",
"Roger Käppeli",
"Roberto Molinaro",
"Emmanuel de Bezenac",
"Siddhartha Mishra"
] | NeurIPS.cc/2024/Conference | 2405.19101 | [
"https://github.com/camlab-ethz/poseidon"
] | https://huggingface.co/papers/2405.19101 | 1 | 0 | 0 | 7 | [
"camlab-ethz/Poseidon-B",
"camlab-ethz/Poseidon-L",
"camlab-ethz/Poseidon-T"
] | [
"camlab-ethz/CE-Gauss",
"camlab-ethz/CE-RM",
"camlab-ethz/CE-CRP",
"camlab-ethz/GCE-RT",
"camlab-ethz/CE-RP",
"camlab-ethz/Wave-Gauss",
"camlab-ethz/ACE",
"camlab-ethz/SE-AF",
"camlab-ethz/Wave-Layer",
"camlab-ethz/NS-BB",
"camlab-ethz/NS-Gauss",
"camlab-ethz/Poisson-Gauss",
"camlab-ethz/Helmholtz",
"camlab-ethz/NS-SL",
"camlab-ethz/CE-RPUI",
"camlab-ethz/NS-SVS",
"camlab-ethz/FNS-KF",
"camlab-ethz/NS-Sines",
"camlab-ethz/NS-PwC"
] | [] | [
"camlab-ethz/Poseidon-B",
"camlab-ethz/Poseidon-L",
"camlab-ethz/Poseidon-T"
] | [
"camlab-ethz/CE-Gauss",
"camlab-ethz/CE-RM",
"camlab-ethz/CE-CRP",
"camlab-ethz/GCE-RT",
"camlab-ethz/CE-RP",
"camlab-ethz/Wave-Gauss",
"camlab-ethz/ACE",
"camlab-ethz/SE-AF",
"camlab-ethz/Wave-Layer",
"camlab-ethz/NS-BB",
"camlab-ethz/NS-Gauss",
"camlab-ethz/Poisson-Gauss",
"camlab-ethz/Helmholtz",
"camlab-ethz/NS-SL",
"camlab-ethz/CE-RPUI",
"camlab-ethz/NS-SVS",
"camlab-ethz/FNS-KF",
"camlab-ethz/NS-Sines",
"camlab-ethz/NS-PwC"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=JBAUg7o8Yv | @inproceedings{
pan2024humansplat,
title={HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors},
author={Panwang Pan and Zhuo Su and Chenguo Lin and Zhen Fan and Yongjie zhang and Zeming Li and Tingting Shen and Yadong MU and Yebin Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JBAUg7o8Yv}
} | Despite recent advancements in high-fidelity human reconstruction techniques, the requirements for densely captured images or time-consuming per-instance optimization significantly hinder their applications in broader scenarios. To tackle these issues, we present **HumanSplat**, which predicts the 3D Gaussian Splatting properties of any human from a single input image in a generalizable manner.
Specifically, HumanSplat comprises a 2D multi-view diffusion model and a latent reconstruction Transformer with human structure priors that adeptly integrate geometric priors and semantic features within a unified framework. A hierarchical loss that incorporates human semantic information is devised to achieve high-fidelity texture modeling and impose stronger constraints on the estimated multiple views. Comprehensive experiments on standard benchmarks and in-the-wild images demonstrate that HumanSplat surpasses existing state-of-the-art methods in achieving photorealistic novel-view synthesis. Project page: https://humansplat.github.io. | HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors | [
"Panwang Pan",
"Zhuo Su",
"Chenguo Lin",
"Zhen Fan",
"Yongjie zhang",
"Zeming Li",
"Tingting Shen",
"Yadong MU",
"Yebin Liu"
] | NeurIPS.cc/2024/Conference | 2406.12459 | [
"https://github.com/humansplat/humansplat.github.io"
] | https://huggingface.co/papers/2406.12459 | 5 | 11 | 1 | 9 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=JAhNsZ9dvG | @inproceedings{
svirschevski2024specexec,
title={SpecExec: Massively Parallel Speculative Decoding For Interactive {LLM} Inference on Consumer Devices},
author={Ruslan Svirschevski and Avner May and Zhuoming Chen and Beidi Chen and Zhihao Jia and Max Ryabinin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JAhNsZ9dvG}
} | As large language models gain widespread adoption, running them efficiently becomes a crucial task. Recent works on LLM inference use speculative decoding to achieve extreme speedups. However, most of these works implicitly design their algorithms for high-end datacenter hardware. In this work, we ask the opposite question: how fast can we run LLMs on consumer machines? Consumer GPUs can no longer fit the largest available models and must offload them to RAM or SSD. With parameter offloading, hundreds or thousands of tokens can be processed in batches within the same time as just one token, making it a natural fit for speculative decoding. We propose SpecExec (Speculative Execution), a simple parallel decoding method that can generate up to 20 tokens per target model iteration for popular LLM families. SpecExec takes the most probable continuations from the draft model to build a "cache" tree for the target model, which then gets validated in a single pass. Using SpecExec, we demonstrate inference of 50B+ parameter LLMs on consumer GPUs with RAM offloading at 4--6 tokens per second with 4-bit quantization or 2--3 tokens per second with 16-bit weights. Our code is available at https://github.com/yandex-research/specexec . | SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices | [
"Ruslan Svirschevski",
"Avner May",
"Zhuoming Chen",
"Beidi Chen",
"Zhihao Jia",
"Max Ryabinin"
] | NeurIPS.cc/2024/Conference | 2406.02532 | [
"https://github.com/yandex-research/specexec"
] | https://huggingface.co/papers/2406.02532 | 1 | 13 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=J8rOw29df2 | @inproceedings{
wang2024on,
title={On the Stability and Generalization of Meta-Learning},
author={Yunjuan Wang and Raman Arora},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J8rOw29df2}
} | We focus on developing a theoretical understanding of meta-learning. Given multiple tasks drawn i.i.d. from some (unknown) task distribution, the goal is to find a good pre-trained model that can be adapted to a new, previously unseen, task with little computational and statistical overhead. We introduce a novel notion of stability for meta-learning algorithms, namely *uniform meta-stability*. We instantiate two uniformly meta-stable learning algorithms based on regularized empirical risk minimization and gradient descent and give explicit generalization bounds for convex learning problems with smooth losses and for weakly convex learning problems with non-smooth losses. Finally, we extend our results to stochastic and adversarially robust variants of our meta-learning algorithm. | On the Stability and Generalization of Meta-Learning | [
"Yunjuan Wang",
"Raman Arora"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=J709rtAUD1 | @inproceedings{
song2024causal,
title={Causal Temporal Representation Learning with Nonstationary Sparse Transition},
author={Xiangchen Song and Zijian Li and Guangyi Chen and Yujia Zheng and Yewen Fan and Xinshuai Dong and Kun Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J709rtAUD1}
} | Causal Temporal Representation Learning (Ctrl) methods aim to identify the temporal causal dynamics of complex nonstationary temporal sequences. Despite the success of existing Ctrl methods, they require either directly observing the domain variables or assuming a Markov prior on them. Such requirements limit the application of these methods in real-world scenarios when we do not have such prior knowledge of the domain variables. To address this problem, this work adopts a sparse transition assumption, aligned with intuitive human understanding, and presents identifiability results from a theoretical perspective. In particular, we explore under what conditions on the significance of the variability of the transitions we can build a model to identify the distribution shifts. Based on the theoretical result, we introduce a novel framework, *Causal Temporal Representation Learning with Nonstationary Sparse Transition* (CtrlNS), designed to leverage the constraints on transition sparsity and conditional independence to reliably identify both distribution shifts and latent factors. Our experimental evaluations on synthetic and real-world datasets demonstrate significant improvements over existing baselines, highlighting the effectiveness of our approach. | Causal Temporal Representation Learning with Nonstationary Sparse Transition | [
"Xiangchen Song",
"Zijian Li",
"Guangyi Chen",
"Yujia Zheng",
"Yewen Fan",
"Xinshuai Dong",
"Kun Zhang"
] | NeurIPS.cc/2024/Conference | 2409.03142 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=J6zHcScAo0 | @inproceedings{
dunefsky2024transcoders,
title={Transcoders find interpretable {LLM} feature circuits},
author={Jacob Dunefsky and Philippe Chlenski and Neel Nanda},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J6zHcScAo0}
} | A key goal in mechanistic interpretability is circuit analysis: finding sparse subgraphs of models corresponding to specific behaviors or capabilities. However, MLP sublayers make fine-grained circuit analysis on transformer-based language models difficult. In particular, interpretable features—such as those found by sparse autoencoders (SAEs)—are typically linear combinations of extremely many neurons, each with its own nonlinearity to account for. Circuit analysis in this setting thus either yields intractably large circuits or fails to disentangle local and global behavior. To address this we explore **transcoders**, which seek to faithfully approximate a densely activating MLP layer with a wider, sparsely-activating MLP layer. We introduce a novel method for using transcoders to perform weights-based circuit analysis through MLP sublayers. The resulting circuits neatly factorize into input-dependent and input-invariant terms. We then successfully train transcoders on language models with 120M, 410M, and 1.4B parameters, and find them to perform at least on par with SAEs in terms of sparsity, faithfulness, and human-interpretability. Finally, we apply transcoders to reverse-engineer unknown circuits in the model, and we obtain novel insights regarding the "greater-than circuit" in GPT2-small. Our results suggest that transcoders can prove effective in decomposing model computations involving MLPs into interpretable circuits. Code is available at https://github.com/jacobdunefsky/transcoder_circuits/ | Transcoders find interpretable LLM feature circuits | [
"Jacob Dunefsky",
"Philippe Chlenski",
"Neel Nanda"
] | NeurIPS.cc/2024/Conference | 2406.11944 | [
"https://github.com/jacobdunefsky/transcoder_circuits"
] | https://huggingface.co/papers/2406.11944 | 1 | 2 | 0 | 3 | [
"google/gemma-scope-2b-pt-transcoders",
"jacobdunefsky/gpt2small-transcoders"
] | [] | [] | [
"google/gemma-scope-2b-pt-transcoders",
"jacobdunefsky/gpt2small-transcoders"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=J6NByZlLNj | @inproceedings{
xia2024waveattack,
title={WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks},
author={Jun Xia and Zhihao Yue and Yingbo Zhou and Zhiwei Ling and Yiyu Shi and Xian Wei and Mingsong Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J6NByZlLNj}
} | Due to the increasing popularity of Artificial Intelligence (AI), more and more backdoor attacks are designed to mislead Deep Neural Network (DNN) predictions by manipulating training samples or processes. Although backdoor attacks have been investigated in various scenarios, they still suffer from the problems of both low fidelity of poisoned samples and non-negligible transfer in latent space, which make them easily identified by existing backdoor detection algorithms. To overcome this weakness, this paper proposes a novel frequency-based backdoor attack method named WaveAttack, which obtains high-frequency image features through Discrete Wavelet Transform (DWT) to generate highly stealthy backdoor triggers. By introducing an asymmetric frequency obfuscation method, our approach adds an adaptive residual to the training and inference stages to improve the impact of triggers, thus further enhancing the effectiveness of WaveAttack. Comprehensive experimental results show that, WaveAttack can not only achieve higher effectiveness than state-of-the-art backdoor attack methods, but also outperform them in the fidelity of images (i.e., by up to 28.27\% improvement in PSNR, 1.61\% improvement in SSIM, and 70.59\% reduction in IS). Our code is available at https://github.com/BililiCode/WaveAttack. | WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks | [
"Jun Xia",
"Zhihao Yue",
"Yingbo Zhou",
"Zhiwei Ling",
"Yiyu Shi",
"Xian Wei",
"Mingsong Chen"
] | NeurIPS.cc/2024/Conference | 2310.11595 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=J42SwBemEA | @inproceedings{
chen2024state,
title={State Chrono Representation for Enhancing Generalization in Reinforcement Learning},
author={Jianda Chen and Wen zheng terence Ng and Zichen Chen and Sinno Jialin Pan and Tianwei Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J42SwBemEA}
} | In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representation space from pixel observations, where the distance between states is measured based on task-relevant features. However, these approaches face challenges in demanding generalization tasks and scenarios with non-informative rewards. This is because they fail to capture sufficient long-term information in the learned representations. To address these challenges, we propose a novel State Chrono Representation (SCR) approach. SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning. It learns state distances within a temporal framework that considers both future dynamics and cumulative rewards over current and long-term future states. Our learning strategy effectively incorporates future behavioral information into the representation space without introducing a significant number of additional parameters for modeling dynamics. Extensive experiments conducted in DeepMind Control and Meta-World environments demonstrate that SCR achieves better performance comparing to other recent metric-based methods in demanding generalization tasks. The codes of SCR are available in https://github.com/jianda-chen/SCR. | State Chrono Representation for Enhancing Generalization in Reinforcement Learning | [
"Jianda Chen",
"Wen zheng terence Ng",
"Zichen Chen",
"Sinno Jialin Pan",
"Tianwei Zhang"
] | NeurIPS.cc/2024/Conference | 2411.06174 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=J3w0AXtEhp | @inproceedings{
liu2024uniform,
title={Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning},
author={Junyan Liu and Yunfan Li and Ruosong Wang and Lin Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J3w0AXtEhp}
} | Existing metrics for reinforcement learning (RL) such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the play of an arbitrarily bad policy at any finite time t. Such a behavior can be highly detrimental in high-stakes applications. This paper introduces a stronger metric, uniform last-iterate (ULI) guarantee, capturing both cumulative and instantaneous performance of RL algorithms. Specifically, ULI characterizes the instantaneous performance since it ensures that the per-round suboptimality of the played policy is bounded by a function, monotonically decreasing w.r.t. (large) round t, preventing revisits to bad policies when sufficient samples are available. We demonstrate that a near-optimal ULI guarantee directly implies near-optimal cumulative performance across aforementioned metrics, but not the other way around.
To examine the achievability of ULI, we first provide two positive results for bandit problems with finite arms, showing that some elimination-based algorithms and high-probability adversarial algorithms with stronger analysis or additional designs, can attain near-optimal ULI guarantees. We also provide a negative result, indicating that optimistic algorithms cannot achieve a near-optimal ULI guarantee. Furthermore, we propose an efficient algorithm for linear bandits with infinitely many arms, which achieves the ULI guarantee, given access to an optimization oracle. Finally, we propose an algorithm that achieves a near-optimal ULI guarantee for the online reinforcement learning setting. | Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning | [
"Junyan Liu",
"Yunfan Li",
"Ruosong Wang",
"Lin Yang"
] | NeurIPS.cc/2024/Conference | 2402.12711 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=J2wOOtkBx0 | @inproceedings{
chen2024diffubox,
title={DiffuBox: Refining 3D Object Detection with Point Diffusion},
author={Xiangyu Chen and Zhenzhen Liu and Katie Z Luo and Siddhartha Datta and Adhitya Polavaram and Yan Wang and Yurong You and Boyi Li and Marco Pavone and Wei-Lun Chao and Mark Campbell and Bharath Hariharan and Kilian Q Weinberger},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J2wOOtkBx0}
} | Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups or geographic locations, often resulting in poor localization accuracy due to domain shift. To overcome this challenge, we introduce a novel diffusion-based box refinement approach. This method employs a domain-agnostic diffusion model, conditioned on the LiDAR points surrounding a coarse bounding box, to simultaneously refine the box's location, size, and orientation. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets, object classes and detectors. Our PyTorch implementation is available at https://github.com/cxy1997/DiffuBox. | DiffuBox: Refining 3D Object Detection with Point Diffusion | [
"Xiangyu Chen",
"Zhenzhen Liu",
"Katie Z Luo",
"Siddhartha Datta",
"Adhitya Polavaram",
"Yan Wang",
"Yurong You",
"Boyi Li",
"Marco Pavone",
"Wei-Lun Chao",
"Mark Campbell",
"Bharath Hariharan",
"Kilian Q Weinberger"
] | NeurIPS.cc/2024/Conference | 2405.16034 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=J2wI2rCG2u | @inproceedings{
shi2024stochastic,
title={Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators},
author={Zekun Shi and Zheyuan Hu and Min Lin and Kenji Kawaguchi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J2wI2rCG2u}
} | Optimizing neural networks with loss that contain high-dimensional and high-order differential operators
is expensive to evaluate with back-propagation due to $\mathcal{O}(d^{k})$ scaling of the derivative tensor size and the $\mathcal{O}(2^{k-1}L)$ scaling in the computation graph, where $d$ is the dimension of the domain, $L$ is the number of ops in the forward computation graph, and $k$ is the derivative order. In previous works, the polynomial scaling in $d$ was addressed by amortizing the computation over the optimization process via randomization. Separately, the exponential scaling in $k$ for univariate functions ($d=1$) was addressed with high-order auto-differentiation (AD). In this work, we show how to efficiently perform arbitrary contraction of the derivative tensor of arbitrary order for multivariate functions, by properly constructing the input tangents to univariate high-order AD, which can be used to efficiently randomize any differential operator.
When applied to Physics-Informed Neural Networks (PINNs), our method provides >1000$\times$ speed-up and >30$\times$ memory reduction over randomization with first-order AD, and we can now solve 1-million-dimensional PDEs in 8 minutes on a single NVIDIA A100 GPU. This work opens the possibility of using high-order differential operators in large-scale problems. | Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators | [
"Zekun Shi",
"Zheyuan Hu",
"Min Lin",
"Kenji Kawaguchi"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=J1Y70keorq | @inproceedings{
hajihashemi2024multimodel,
title={Multi-model Ensemble Conformal Prediction in Dynamic Environments},
author={Erfan Hajihashemi and Yanning Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J1Y70keorq}
} | Conformal prediction is an uncertainty quantification method that constructs a prediction set for a previously unseen datum, ensuring the true label is included with a predetermined coverage probability. Adaptive conformal prediction has been developed to address data distribution shifts in dynamic environments. However, the efficiency of prediction sets varies depending on the learning model used. Employing a single fixed model may not consistently offer the best performance in dynamic environments with unknown data distribution shifts. To address this issue, we introduce a novel adaptive conformal prediction framework, where the model used for creating prediction sets is selected ‘on the fly’ from multiple candidate models. The proposed algorithm is proven to achieve strongly adaptive regret over all intervals while maintaining valid coverage. Experiments on both real and synthetic datasets corroborate that the proposed approach consistently yields more efficient prediction sets while maintaining valid coverage, outperforming alternative methods. | Multi-model Ensemble Conformal Prediction in Dynamic Environments | [
"Erfan Hajihashemi",
"Yanning Shen"
] | NeurIPS.cc/2024/Conference | 2411.03678 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=J0Itri0UiN | @inproceedings{
zhou2024counterfactual,
title={Counterfactual Fairness by Combining Factual and Counterfactual Predictions},
author={Zeyu Zhou and Tianci Liu and Ruqi Bai and Jing Gao and Murat Kocaoglu and David I. Inouye},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=J0Itri0UiN}
} | In high-stakes domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns.
This work focuses on Counterfactual Fairness (CF), which posits that an ML model's outcome on any individual should remain unchanged if they had belonged to a different demographic group.
Previous works have proposed methods that guarantee CF.
Notwithstanding, their effects on the model's predictive performance remain largely unclear.
To fill this gap, we provide a theoretical study on the inherent trade-off between CF and predictive performance in a model-agnostic manner.
We first propose a simple but effective method to cast an optimal but potentially unfair predictor into a fair one with a minimal loss of performance.
By analyzing the excess risk incurred by perfect CF, we quantify this inherent trade-off.
Further analysis on our method's performance with access to only incomplete causal knowledge is also conducted.
Built upon this, we propose a practical algorithm that can be applied in such scenarios.
Experiments on both synthetic and semi-synthetic datasets demonstrate the validity of our analysis and methods. | Counterfactual Fairness by Combining Factual and Counterfactual Predictions | [
"Zeyu Zhou",
"Tianci Liu",
"Ruqi Bai",
"Jing Gao",
"Murat Kocaoglu",
"David I. Inouye"
] | NeurIPS.cc/2024/Conference | 2409.01977 | [
"https://github.com/inouye-lab/pcf"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IxazPgGF8h | @inproceedings{
liu2024chatcam,
title={ChatCam: Empowering Camera Control through Conversational {AI}},
author={Xinhang Liu and Yu-Wing Tai and Chi-Keung Tang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IxazPgGF8h}
} | Cinematographers adeptly capture the essence of the world, crafting compelling visual narratives through intricate camera movements. Witnessing the strides made by large language models in perceiving and interacting with the 3D world, this study explores their capability to control cameras with human language guidance. We introduce ChatCam, a system that navigates camera movements through conversations with users, mimicking a professional cinematographer's workflow. To achieve this, we propose CineGPT, a GPT-based autoregressive model for text-conditioned camera trajectory generation. We also develop an Anchor Determinator to ensure precise camera trajectory placement. ChatCam understands user requests and employs our proposed tools to generate trajectories, which can be used to render high-quality video footage on radiance field representations. Our experiments, including comparisons to state-of-the-art approaches and user studies, demonstrate our approach's ability to interpret and execute complex instructions for camera operation, showing promising applications in real-world production settings. Project page: https://xinhangliu.com/chatcam. | ChatCam: Empowering Camera Control through Conversational AI | [
"Xinhang Liu",
"Yu-Wing Tai",
"Chi-Keung Tang"
] | NeurIPS.cc/2024/Conference | 2409.17331 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IxRf7Q3s5e | @inproceedings{
esteves2024neuralsolver,
title={NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks},
author={Bernardo Esteves and Miguel Vasco and Francisco S. Melo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IxRf7Q3s5e}
} | We contribute NeuralSolver, a novel recurrent solver that can efficiently and consistently extrapolate, i.e., learn algorithms from smaller problems (in terms of observation size) and execute those algorithms in large problems. Contrary to previous recurrent solvers, NeuralSolver can be naturally applied in both same-size problems, where the input and output sizes are the same, and in different-size problems, where the size of the input and output differ. To allow for this versatility, we design NeuralSolver with three main components: a recurrent module, that iteratively processes input information at different scales, a processing module, responsible for aggregating the previously processed information, and a curriculum-based training scheme, that improves the extrapolation performance of the method.
To evaluate our method we introduce a set of novel different-size tasks and we show that NeuralSolver consistently outperforms the prior state-of-the-art recurrent solvers in extrapolating to larger problems, considering smaller training problems and requiring less parameters than other approaches. | NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks | [
"Bernardo Esteves",
"Miguel Vasco",
"Francisco S. Melo"
] | NeurIPS.cc/2024/Conference | 2402.15393 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IxEhb4NCvy | @inproceedings{
lian2024ssdm,
title={{SSDM}: Scalable Speech Dysfluency Modeling},
author={Jiachen Lian and Xuanru Zhou and Zoe Ezzes and Jet M.J. Vonk and Brittany T. Morin and David Paul Galang Baquirin and Zachary A. Miller and Maria Luisa Gorno-Tempini and Gopala Anumanchipalli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IxEhb4NCvy}
} | Speech dysfluency modeling is the core module for spoken language learning, and speech therapy. However, there are three challenges. First, current state-of-the-art solutions~~\cite{lian2023unconstrained-udm, lian-anumanchipalli-2024-towards-hudm} suffer from poor scalability. Second, there is a lack of a large-scale dysfluency corpus. Third, there is not an effective learning framework. In this paper, we propose \textit{SSDM: Scalable Speech Dysfluency Modeling}, which (1) adopts articulatory gestures as scalable forced alignment; (2) introduces connectionist subsequence aligner (CSA) to achieve dysfluency alignment; (3) introduces a large-scale simulated dysfluency corpus called Libri-Dys; and (4) develops an end-to-end system by leveraging the power of large language models (LLMs). We expect SSDM to serve as a standard in the area of dysfluency modeling. Demo is available at \url{https://berkeley-speech-group.github.io/SSDM/}. | SSDM: Scalable Speech Dysfluency Modeling | [
"Jiachen Lian",
"Xuanru Zhou",
"Zoe Ezzes",
"Jet M.J. Vonk",
"Brittany T. Morin",
"David Paul Galang Baquirin",
"Zachary A. Miller",
"Maria Luisa Gorno-Tempini",
"Gopala Anumanchipalli"
] | NeurIPS.cc/2024/Conference | 2408.16221 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IwNTiNPxFt | @inproceedings{
wang2024stablepose,
title={Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation},
author={Jiajun Wang and MORTEZA GHAHREMANI and Yitong Li and Bj{\"o}rn Ommer and Christian Wachinger},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IwNTiNPxFt}
} | Controllable text-to-image (T2I) diffusion models have shown impressive performance in generating high-quality visual content through the incorporation of various conditions. Current methods, however, exhibit limited performance when guided by skeleton human poses, especially in complex pose conditions such as side or rear perspectives of human figures. To address this issue, we present Stable-Pose, a novel adapter model that introduces a coarse-to-fine attention masking strategy into a vision Transformer (ViT) to gain accurate pose guidance for T2I models. Stable-Pose is designed to adeptly handle pose conditions within pre-trained Stable Diffusion, providing a refined and efficient way of aligning pose representation during image synthesis. We leverage the query-key self-attention mechanism of ViTs to explore the interconnections among different anatomical parts in human pose skeletons. Masked pose images are used to smoothly refine the attention maps based on target pose-related features in a hierarchical manner, transitioning from coarse to fine levels.
Additionally, our loss function is formulated to allocate increased emphasis to the pose region, thereby augmenting the model's precision in capturing intricate pose details. We assessed the performance of Stable-Pose across five public datasets under a wide range of indoor and outdoor human pose scenarios. Stable-Pose achieved an AP score of 57.1 in the LAION-Human dataset, marking around 13\% improvement over the established technique ControlNet. The project link and code is available at https://github.com/ai-med/StablePose. | Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation | [
"Jiajun Wang",
"MORTEZA GHAHREMANI",
"Yitong Li",
"Björn Ommer",
"Christian Wachinger"
] | NeurIPS.cc/2024/Conference | 2406.02485 | [
"https://github.com/ai-med/stablepose"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ItzD2Cnu9y | @inproceedings{
adeli2024randomized,
title={Randomized Sparse Matrix Compression for Large-Scale Constrained Optimization in Cancer Radiotherapy},
author={Shima Adeli and Mojtaba Tefagh and Gourav Jhanwar and Masoud Zarepisheh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ItzD2Cnu9y}
} | Radiation therapy, treating over half of all cancer patients, involves using specialized machines to direct high-energy beams at tumors, aiming to damage cancer cells while minimizing harm to nearby healthy tissues. Customizing the shape and intensity of radiation beams for each patient leads to solving large-scale constrained optimization problems that need to be solved within tight clinical time-frame. At the core of these challenges is a large matrix that is commonly sparsified for computational efficiency by neglecting small elements. Such a crude approximation can degrade the quality of treatment, potentially causing unnecessary radiation exposure to healthy tissues—this may lead to significant radiation-induced side effects—or delivering inadequate radiation to the tumor, which is crucial for effective tumor treatment. In this work, we demonstrate, for the first time, that randomized sketch tools can effectively sparsify this matrix without sacrificing treatment quality. We also develop a novel randomized sketch method with desirable theoretical guarantees that outperforms existing techniques in practical application. Beyond developing a novel randomized sketch method, this work emphasizes the potential of harnessing scientific computing tools, crucial in today's big data analysis, to tackle computationally intensive challenges in healthcare. The application of these tools could have a profound impact on the lives of numerous cancer patients. Code and sample data available at https://github.com/PortPy-Project/CompressRTP | Randomized Sparse Matrix Compression for Large-Scale Constrained Optimization in Cancer Radiotherapy | [
"Shima Adeli",
"Mojtaba Tefagh",
"Gourav Jhanwar",
"Masoud Zarepisheh"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Iq2IAWozNr | @inproceedings{
cadei2024smoke,
title={Smoke and Mirrors in Causal Downstream Tasks},
author={Riccardo Cadei and Lukas Lindorfer and Sylvia Cremer and Cordelia Schmid and Francesco Locatello},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Iq2IAWozNr}
} | Machine Learning and AI have the potential to transform data-driven scientific discovery, enabling accurate predictions for several scientific phenomena. As many scientific questions are inherently causal, this paper looks at the causal inference task of treatment effect estimation, where the outcome of interest is recorded in high-dimensional observations in a Randomized Controlled Trial (RCT). Despite being the simplest possible causal setting and a perfect fit for deep learning, we theoretically find that many common choices in the literature may lead to biased estimates. To test the practical impact of these considerations, we recorded ISTAnt, the first real-world benchmark for causal inference downstream tasks on high-dimensional observations as an RCT studying how garden ants (Lasius neglectus) respond to microparticles applied onto their colony members by hygienic grooming. Comparing 6 480 models fine-tuned from state-of-the-art visual backbones, we find that the sampling and modeling choices significantly affect the accuracy of the causal estimate, and that classification accuracy is not a proxy thereof. We further validated the analysis, repeating it on a synthetically generated visual data set controlling the causal model. Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones. Further, we highlight guidelines for representation learning methods to help answer causal questions in the sciences. | Smoke and Mirrors in Causal Downstream Tasks | [
"Riccardo Cadei",
"Lukas Lindorfer",
"Sylvia Cremer",
"Cordelia Schmid",
"Francesco Locatello"
] | NeurIPS.cc/2024/Conference | 2405.17151 | [
"https://github.com/causallearningai/istant"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IpHB5RC3za | @inproceedings{
li2024realtime,
title={Real-time Stereo-based 3D Object Detection for Streaming Perception},
author={Changcai Li and Zonghua Gu and Gang Chen and Libo Huang and Wei Zhang and Huihui Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IpHB5RC3za}
} | The ability to promptly respond to environmental changes is crucial for the perception system of autonomous driving. Recently, a new task called streaming perception was proposed. It jointly evaluate the latency and accuracy into a single metric for video online perception. In this work, we introduce StreamDSGN, the first real-time stereo-based 3D object detection framework designed for streaming perception. StreamDSGN is an end-to-end framework that directly predicts the 3D properties of objects in the next moment by leveraging historical information, thereby alleviating the accuracy degradation of streaming perception. Further, StreamDSGN applies three strategies to enhance the perception accuracy: (1) A feature-flow-based fusion method, which generates a pseudo-next feature at the current moment to address the misalignment issue between feature and ground truth. (2) An extra regression loss for explicit supervision of object motion consistency in consecutive frames. (3) A large kernel backbone with a large receptive field for effectively capturing long-range spatial contextual features caused by changes in object positions. Experiments on the KITTI Tracking dataset show that, compared with the strong baseline, StreamDSGN significantly improves the streaming average precision by up to 4.33%. Our code is available at https://github.com/weiyangdaren/streamDSGN-pytorch. | Real-time Stereo-based 3D Object Detection for Streaming Perception | [
"Changcai Li",
"Zonghua Gu",
"Gang Chen",
"Libo Huang",
"Wei Zhang",
"Huihui Zhou"
] | NeurIPS.cc/2024/Conference | 2410.12394 | [
"https://github.com/weiyangdaren/streamdsgn-pytorch"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Ioabr42B44 | @inproceedings{
yao2024dense,
title={Dense Connector for {MLLM}s},
author={Huanjin Yao and Wenhao Wu and Taojiannan Yang and YuXin Song and Mengxi Zhang and Haocheng Feng and Yifan Sun and Zhiheng Li and Wanli Ouyang and Jingdong Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ioabr42B44}
} | *Do we fully leverage the potential of visual encoder in Multimodal Large Language Models (MLLMs)?* The recent outstanding performance of MLLMs in multimodal understanding has garnered broad attention from both academia and industry. In the current MLLM rat race, the focus seems to be predominantly on the linguistic side. We witness the rise of larger and higher-quality instruction datasets, as well as the involvement of larger-sized LLMs. Yet, scant attention has been directed towards the visual signals utilized by MLLMs, often assumed to be the final high-level features extracted by a frozen visual encoder. In this paper, we introduce the **Dense Connector** - a simple, effective, and plug-and-play vision-language connector that significantly enhances existing MLLMs by leveraging multi-layer visual features, with minimal additional computational overhead. Building on this, we also propose the Efficient Dense Connector, which achieves performance comparable to LLaVA-v1.5 with only 25% of the visual tokens. Furthermore, our model, trained solely on images, showcases remarkable zero-shot capabilities in video understanding as well. Experimental results across various vision encoders, image resolutions, training dataset scales, varying sizes of LLMs (2.7B→70B), and diverse architectures of MLLMs (e.g., LLaVA-v1.5, LLaVA-NeXT and Mini-Gemini) validate the versatility and scalability of our approach, achieving state-of-the-art performance across 19 image and video benchmarks. We hope that this work will provide valuable experience and serve as a basic module for future MLLM development. Code is available at https://github.com/HJYao00/DenseConnector. | Dense Connector for MLLMs | [
"Huanjin Yao",
"Wenhao Wu",
"Taojiannan Yang",
"YuXin Song",
"Mengxi Zhang",
"Haocheng Feng",
"Yifan Sun",
"Zhiheng Li",
"Wanli Ouyang",
"Jingdong Wang"
] | NeurIPS.cc/2024/Conference | 2405.13800 | [
"https://github.com/HJYao00/DenseConnector"
] | https://huggingface.co/papers/2405.13800 | 5 | 21 | 4 | 10 | [
"HuanjinYao/DenseConnector-v1.5-8B",
"HuanjinYao/DenseConnector-v1.5-13B",
"HuanjinYao/DenseConnector-v1.5-SigLIP-7B-AnyRes",
"HuanjinYao/DenseConnector-v1.5-7B",
"HuanjinYao/DenseConnector-with-mgm-7B"
] | [] | [
"merve/vision_papers",
"HuanjinYao/DenseConnector-v1.5-8B",
"velaia/vision_papers"
] | [
"HuanjinYao/DenseConnector-v1.5-8B",
"HuanjinYao/DenseConnector-v1.5-13B",
"HuanjinYao/DenseConnector-v1.5-SigLIP-7B-AnyRes",
"HuanjinYao/DenseConnector-v1.5-7B",
"HuanjinYao/DenseConnector-with-mgm-7B"
] | [] | [
"merve/vision_papers",
"HuanjinYao/DenseConnector-v1.5-8B",
"velaia/vision_papers"
] | 1 | poster |
null | https://openreview.net/forum?id=IoRT7EhFap | @inproceedings{
fang2024addressing,
title={Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning},
author={Ronglong Fang and Yuesheng Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IoRT7EhFap}
} | Deep neural networks (DNNs) have showcased their remarkable precision in approximating smooth functions. However, they suffer from the {\it spectral bias}, wherein DNNs typically exhibit a tendency to prioritize the learning of lower-frequency components of a function, struggling to effectively capture its high-frequency features. This paper is to address this issue. Notice that a function having only low frequency components may be well-represented by a shallow neural network (SNN), a network having only a few layers. By observing that composition of low frequency functions can effectively approximate a high-frequency function, we propose to learn a function containing high-frequency components by composing several SNNs, each of which learns certain low-frequency information from the given data. We implement the proposed idea by exploiting the multi-grade deep learning (MGDL) model, a recently introduced model that trains a DNN incrementally, grade by grade, a current grade learning from the residue of the previous grade only an SNN (with trainable parameters) composed with the SNNs (with fixed parameters) trained in the preceding grades as features. We apply MGDL to synthetic, manifold, colored images, and MNIST datasets, all characterized by presence of high-frequency features. Our study reveals that MGDL excels at representing functions containing high-frequency information. Specifically, the neural networks learned in each grade adeptly capture some low-frequency information, allowing their compositions with SNNs learned in the previous grades effectively representing the high-frequency features. Our experimental results underscore the efficacy of MGDL in addressing the spectral bias inherent in DNNs. By leveraging MGDL, we offer insights into overcoming spectral bias limitation of DNNs, thereby enhancing the performance and applicability of deep learning models in tasks requiring the representation of high-frequency information. This study confirms that the proposed method offers a promising solution to address the spectral bias of DNNs. The code is available on GitHub: \href{https://github.com/Ronglong-Fang/AddressingSpectralBiasviaMGDL}{\texttt{Addressing Spectral Bias via MGDL}}. | Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning | [
"Ronglong Fang",
"Yuesheng Xu"
] | NeurIPS.cc/2024/Conference | 2410.16105 | [
"https://github.com/ronglong-fang/addressingspectralbiasviamgdl"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Io1qKqCVIK | @inproceedings{
son2024dmesh,
title={{DM}esh: A Differentiable Mesh Representation},
author={Sanghyun Son and Matheus Gadelha and Yang Zhou and Zexiang Xu and Ming Lin and Yi Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Io1qKqCVIK}
} | We present a differentiable representation, DMesh, for general 3D triangular meshes. DMesh considers both the geometry and connectivity information of a mesh. In our design, we first get a set of convex tetrahedra that compactly tessellates the domain based on Weighted Delaunay Triangulation (WDT), and select triangular faces on the tetrahedra to define the final mesh. We formulate probability of faces to exist on the actual surface in a differentiable manner based on the WDT. This enables DMesh to represent meshes of various topology in a differentiable way, and allows us to reconstruct the mesh under various observations, such as point clouds and multi-view images using gradient-based optimization. We publicize the source code and supplementary material at our project page (https://sonsang.github.io/dmesh-project). | DMesh: A Differentiable Mesh Representation | [
"Sanghyun Son",
"Matheus Gadelha",
"Yang Zhou",
"Zexiang Xu",
"Ming Lin",
"Yi Zhou"
] | NeurIPS.cc/2024/Conference | 2404.13445 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IlIDNMvwmX | @inproceedings{
hao2024lmht,
title={{LM}-{HT} {SNN}: Enhancing the Performance of {SNN} to {ANN} Counterpart through Learnable Multi-hierarchical Threshold Model},
author={Zecheng Hao and Xinyu Shi and Yujia Liu and Zhaofei Yu and Tiejun Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IlIDNMvwmX}
} | Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more energy-efficient manner. However, despite previous efforts to optimize the learning algorithm of SNNs through various methods, SNNs still lag behind ANNs in terms of performance. The recently proposed multi-threshold model provides more possibilities for further enhancing the learning capability of SNNs. In this paper, we rigorously analyze the relationship among the multi-threshold model, vanilla spiking model and quantized ANNs from a mathematical perspective, then propose a novel LM-HT model, which is an equidistant multi-threshold model that can dynamically regulate the global input current and membrane potential leakage on the time dimension. The LM-HT model can also be transformed into a vanilla single threshold model through reparameterization, thereby achieving more flexible hardware deployment. In addition, we note that the LM-HT model can seamlessly integrate with ANN-SNN Conversion framework under special initialization. This novel hybrid learning framework can effectively improve the relatively poor performance of converted SNNs under low time latency. Extensive experimental results have demonstrated that our model can outperform previous state-of-the-art works on various types of datasets, which promote SNNs to achieve a brand-new level of performance comparable to quantized ANNs. Code is available at https://github.com/hzc1208/LMHT_SNN. | LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model | [
"Zecheng Hao",
"Xinyu Shi",
"Yujia Liu",
"Zhaofei Yu",
"Tiejun Huang"
] | NeurIPS.cc/2024/Conference | 2402.00411 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IjHrALdQNP | @inproceedings{
yuan2024gamap,
title={{GAM}ap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance},
author={Shuaihang Yuan and Hao Huang and Yu Hao and Congcong Wen and Anthony Tzes and Yi Fang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IjHrALdQNP}
} | Zero-Shot Object Goal Navigation (ZS-OGN) enables robots to navigate toward objects of unseen categories without prior training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only partial objects are observed or detailed and functional representations of the environment are lacking. To resolve the above two issues, we propose \textit{Geometric-part and Affordance Maps} (GAMap), a novel method that integrates object parts and affordance attributes for navigation guidance. Our method includes a multi-scale scoring approach to capture geometric-part and affordance attributes of objects at different scales. Comprehensive experiments conducted on the HM3D and Gibson benchmark datasets demonstrate improvements in Success Rates and Success weighted by Path Length, underscoring the efficacy of our geometric-part and affordance-guided navigation approach in enhancing robot autonomy and versatility, without any additional task-specific training or fine-tuning with the semantics of unseen objects and/or the locomotions of the robot. | GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance | [
"Shuaihang Yuan",
"Hao Huang",
"Yu Hao",
"Congcong Wen",
"Anthony Tzes",
"Yi Fang"
] | NeurIPS.cc/2024/Conference | 2410.23978 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IgU8gMKy4D | @inproceedings{
hawke2024contrastive,
title={Contrastive dimension reduction: when and how?},
author={Sam Hawke and Yueen Ma and Didong Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IgU8gMKy4D}
} | Dimension reduction (DR) is an important and widely studied technique in exploratory data analysis. However, traditional DR methods are not applicable to datasets with with a contrastive structure, where data are split into a foreground group of interest (case or treatment group), and a background group (control group). This type of data, common in biomedical studies, necessitates contrastive dimension reduction (CDR) methods to effectively capture information unique to or enriched in the foreground group relative to the background group. Despite the development of various CDR methods, two critical questions remain underexplored: when should these methods be applied, and how can the information unique to the foreground group be quantified? In this work, we address these gaps by proposing a hypothesis test to determine the existence of contrastive information, and introducing a contrastive dimension estimator (CDE) to quantify the unique components in the foreground group. We provide theoretical support for our methods and validate their effectiveness through extensive simulated, semi-simulated, and real experiments involving images, gene expressions, protein expressions, and medical sensors, demonstrating their ability to identify the unique information in the foreground group. | Contrastive dimension reduction: when and how? | [
"Sam Hawke",
"Yueen Ma",
"Didong Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=IfZwSRpqHl | @inproceedings{
mustafa2024dynamic,
title={Dynamic Rescaling for Training {GNN}s},
author={Nimrah Mustafa and Rebekka Burkholz},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IfZwSRpqHl}
} | Graph neural networks (GNNs) with a rescale invariance, such as GATs, can be re-parameterized during optimization through dynamic rescaling of network parameters and gradients while keeping the loss invariant. In this work, we explore dynamic rescaling as a tool to influence GNN training dynamics in two key ways: i) balancing the network with respect to various criteria, and ii) controlling the relative learning speeds of different layers. We gain novel insights, unique to GNNs, that reveal distinct training modes for different tasks. For heterophilic graphs, achieving balance based on relative gradients leads to faster training and better generalization. In contrast, homophilic graphs benefit from delaying the learning of later layers. Additionally, we show that training in balance supports larger learning rates, which can improve generalization. Moreover, controlling layer-wise training speeds is linked to grokking-like phenomena, which may be of independent interest. | Dynamic Rescaling for Training GNNs | [
"Nimrah Mustafa",
"Rebekka Burkholz"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=IdtoJVWVnX | @inproceedings{
wan2024teach,
title={Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization},
author={Xingchen Wan and Ruoxi Sun and Hootan Nakhost and Sercan O Arik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IdtoJVWVnX}
} | Large language models have demonstrated remarkable capabilities but their performance is heavily reliant on effective prompt engineering. Automatic prompt optimization (APO) methods are designed to automate this and can be broadly categorized into those targeting instructions (instruction optimization, IO) vs. those targeting exemplars (exemplar optimization, EO). Despite their shared objective, these have evolved rather independently, with IO receiving more research attention recently. This paper seeks to bridge this gap by comprehensively comparing the performance of representative IO and EO techniques both isolation and combination on a diverse set of challenging tasks. Our findings reveal that intelligently reusing model-generated input-output pairs obtained from evaluating prompts on the validation set as exemplars, consistently improves performance on top of IO methods but is currently under-investigated. We also find that despite the recent focus on IO, how we select exemplars can outweigh how we optimize instructions, with EO strategies as simple as random search outperforming state-of-the-art IO methods with seed instructions without any optimization. Moreover, we observe a synergy between EO and IO, with optimal combinations surpassing the individual contributions. We conclude that studying exemplar optimization both as a standalone method and its optimal combination with instruction optimization remain a crucial aspect of APO and deserve greater consideration in future research, even in the era of highly capable instruction-following models. | Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization | [
"Xingchen Wan",
"Ruoxi Sun",
"Hootan Nakhost",
"Sercan O Arik"
] | NeurIPS.cc/2024/Conference | 2406.15708 | [
""
] | https://huggingface.co/papers/2406.15708 | 0 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=IdQuUYMA1t | @inproceedings{
shin2024dash,
title={{DASH}: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity},
author={Baekrok Shin and Junsoo Oh and Hanseul Cho and Chulhee Yun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IdQuUYMA1t}
} | Warm-starting neural network training by initializing networks with previously learned weights is appealing, as practical neural networks are often deployed under a continuous influx of new data. However, it often leads to *loss of plasticity*, where the network loses its ability to learn new information, resulting in worse generalization than training from scratch. This occurs even under stationary data distributions, and its underlying mechanism is poorly understood. We develop a framework emulating real-world neural network training and identify noise memorization as the primary cause of plasticity loss when warm-starting on stationary data. Motivated by this, we propose **Direction-Aware SHrinking (DASH)**, a method aiming to mitigate plasticity loss by selectively forgetting memorized noise while preserving learned features. We validate our approach on vision tasks, demonstrating improvements in test accuracy and training efficiency. | DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity | [
"Baekrok Shin",
"Junsoo Oh",
"Hanseul Cho",
"Chulhee Yun"
] | NeurIPS.cc/2024/Conference | 2410.23495 | [
"https://github.com/NAVER-INTEL-Co-Lab/gaudi-dash"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IdIVfzjPK4 | @inproceedings{
liu2024arc,
title={{ARC}: A Generalist Graph Anomaly Detector with In-Context Learning},
author={Yixin Liu and Shiyuan Li and Yu Zheng and Qingfeng Chen and Chengqi Zhang and Shirui Pan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IdIVfzjPK4}
} | Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods necessitate training specific to each dataset, resulting in high training costs, substantial data requirements, and limited generalizability when being applied to new datasets and domains. To address these limitations, this paper proposes ARC, a generalist GAD approach that enables a ``one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. Equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset using few-shot normal samples at the inference stage, without the need for retraining or fine-tuning on the target dataset. ARC comprises three components that are well-crafted for capturing universal graph anomaly patterns: 1) smoothness-based feature **A**lignment module that unifies the features of different datasets into a common and anomaly-sensitive space; 2) ego-neighbor **R**esidual graph encoder that learns abnormality-related node embeddings; and 3) cross-attentive in-**C**ontext anomaly scoring module that predicts node abnormality by leveraging few-shot normal samples. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC. | ARC: A Generalist Graph Anomaly Detector with In-Context Learning | [
"Yixin Liu",
"Shiyuan Li",
"Yu Zheng",
"Qingfeng Chen",
"Chengqi Zhang",
"Shirui Pan"
] | NeurIPS.cc/2024/Conference | 2405.16771 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IbIB8SBKFV | @inproceedings{
zou2024improving,
title={Improving Alignment and Robustness with Circuit Breakers},
author={Andy Zou and Long Phan and Justin Wang and Derek Duenas and Maxwell Lin and Maksym Andriushchenko and J Zico Kolter and Matt Fredrikson and Dan Hendrycks},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IbIB8SBKFV}
} | AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interrupts the models as they respond with harmful outputs with "circuit breakers." Existing techniques aimed at improving alignment, such as refusal training, are often bypassed. Techniques such as adversarial training try to plug these holes by countering specific attacks. As an alternative to refusal training and adversarial training, circuit-breaking directly controls the representations that are responsible for harmful outputs in the first place. Our technique can be applied to both text-only and multimodal language models to prevent the generation of harmful outputs without sacrificing utility -- even in the presence of powerful unseen attacks. Notably, while adversarial robustness in standalone image recognition remains an open challenge, circuit breakers allow the larger multimodal system to reliably withstand image "hijacks" that aim to produce harmful content. Finally, we extend our approach to AI agents, demonstrating considerable reductions in the rate of harmful actions when they are under attack. Our approach represents a significant step forward in the development of reliable safeguards to harmful behavior and adversarial attacks. | Improving Alignment and Robustness with Circuit Breakers | [
"Andy Zou",
"Long Phan",
"Justin Wang",
"Derek Duenas",
"Maxwell Lin",
"Maksym Andriushchenko",
"J Zico Kolter",
"Matt Fredrikson",
"Dan Hendrycks"
] | NeurIPS.cc/2024/Conference | 2406.04313 | [
"https://github.com/blackswan-ai/short-circuiting"
] | https://huggingface.co/papers/2406.04313 | 0 | 1 | 0 | 10 | [
"GraySwanAI/Llama-3-8B-Instruct-RR",
"GraySwanAI/Mistral-7B-Instruct-RR",
"RichardErkhov/GraySwanAI_-_Llama-3-8B-Instruct-RR-gguf",
"RichardErkhov/GraySwanAI_-_Mistral-7B-Instruct-RR-gguf",
"GraySwanAI/llava-v1.6-mistral-7b-hf-RR"
] | [] | [
"featherless-ai/try-this-model",
"Granther/try-this-model",
"Darok/Featherless-Feud",
"emekaboris/try-this-model",
"SC999/NV_Nemotron"
] | [
"GraySwanAI/Llama-3-8B-Instruct-RR",
"GraySwanAI/Mistral-7B-Instruct-RR",
"RichardErkhov/GraySwanAI_-_Llama-3-8B-Instruct-RR-gguf",
"RichardErkhov/GraySwanAI_-_Mistral-7B-Instruct-RR-gguf",
"GraySwanAI/llava-v1.6-mistral-7b-hf-RR"
] | [] | [
"featherless-ai/try-this-model",
"Granther/try-this-model",
"Darok/Featherless-Feud",
"emekaboris/try-this-model",
"SC999/NV_Nemotron"
] | 1 | poster |
null | https://openreview.net/forum?id=Ib2iHIJRTh | @inproceedings{
cachay2024probablistic,
title={Probablistic Emulation of a Global Climate Model with Spherical {DY}ffusion},
author={Salva R{\"u}hling Cachay and Brian Henn and Oliver Watt-Meyer and Christopher S. Bretherton and Rose Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ib2iHIJRTh}
} | Data-driven deep learning models are transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where the complexity of the data and long inference rollouts pose significant challenges. Here, we present the first conditional generative model that produces accurate and physically consistent global climate ensemble simulations by emulating a coarse version of the United States' primary operational global forecast model, FV3GFS.
Our model integrates the dynamics-informed diffusion framework (DYffusion) with the Spherical Fourier Neural Operator (SFNO) architecture, enabling stable 100-year simulations at 6-hourly timesteps while maintaining low computational overhead compared to single-step deterministic baselines.
The model achieves near gold-standard performance for climate model emulation, outperforming existing approaches and demonstrating promising ensemble skill.
This work represents a significant advance towards efficient, data-driven climate simulations that can enhance our understanding of the climate system and inform adaptation strategies. Code is available at [https://github.com/Rose-STL-Lab/spherical-dyffusion](https://github.com/Rose-STL-Lab/spherical-dyffusion). | Probablistic Emulation of a Global Climate Model with Spherical DYffusion | [
"Salva Rühling Cachay",
"Brian Henn",
"Oliver Watt-Meyer",
"Christopher S. Bretherton",
"Rose Yu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=IXRa8adMHX | @inproceedings{
tyurin2024on,
title={On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization},
author={Alexander Tyurin and Peter Richt{\'a}rik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IXRa8adMHX}
} | We consider the decentralized stochastic asynchronous optimization setup, where many workers asynchronously calculate stochastic gradients and asynchronously communicate with each other using edges in a multigraph. For both homogeneous and heterogeneous setups, we prove new time complexity lower bounds under the assumption that computation and communication speeds are bounded by constants. After that, we developed a new nearly optimal method, Fragile SGD, and a new optimal method, Amelie SGD, that converge with arbitrary heterogeneous computation and communication speeds and match our lower bounds (up to a logarithmic factor in the homogeneous setting). Our time complexities are new, nearly optimal, and provably improve all previous asynchronous/synchronous stochastic methods in the decentralized setup. | On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization | [
"Alexander Tyurin",
"Peter Richtárik"
] | NeurIPS.cc/2024/Conference | 2405.16218 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IVqzbuLfoL | @inproceedings{
ye2024d,
title={3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning},
author={Zhifan Ye and Chenxi Wan and Chaojian Li and Jihoon Hong and Sixu Li and Leshu Li and Yongan Zhang and Yingyan Celine Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IVqzbuLfoL}
} | 3D Gaussian splatting has recently emerged as a promising technique for novel view synthesis from sparse image sets, yet comes at the cost of requiring millions of 3D Gaussian primitives to reconstruct each 3D scene. This largely limits its application to resource-constrained devices and applications.
Despite advances in Gaussian pruning techniques that aim to remove individual 3D Gaussian primitives, the significant reduction in primitives often fails to translate into commensurate increases in rendering speed, impeding efficiency and practical deployment. We identify that this discrepancy arises due to the overlooked impact of fragment count per Gaussian (i.e., the number of pixels each Gaussian is projected onto). To bridge this gap and meet the growing demands for efficient on-device 3D Gaussian rendering, we propose fragment pruning, an orthogonal enhancement to existing pruning methods that can significantly accelerate rendering by selectively pruning fragments within each Gaussian. Our pruning framework dynamically optimizes the pruning threshold for each Gaussian, markedly improving rendering speed and quality. Extensive experiments in both static and dynamic scenes validate the effectiveness of our approach. For instance, by integrating our fragment pruning technique with state-of-the-art Gaussian pruning methods, we achieve up to a 1.71$\times$ speedup on an edge GPU device, the Jetson Orin NX, and enhance rendering quality by an average of 0.16 PSNR on the Tanks\&Temples dataset. Our code is available at https://github.com/GATECH-EIC/Fragment-Pruning. | 3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning | [
"Zhifan Ye",
"Chenxi Wan",
"Chaojian Li",
"Jihoon Hong",
"Sixu Li",
"Leshu Li",
"Yongan Zhang",
"Yingyan Celine Lin"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=IVjs67Xa44 | @inproceedings{
hosseini2024putting,
title={Putting Gale \& Shapley to Work: Guaranteeing Stability Through Learning},
author={Hadi Hosseini and Sanjukta Roy and Duohan Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IVjs67Xa44}
} | Two-sided matching markets describe a large class of problems wherein participants from one side of the market must be matched to those from the other side according to their preferences. In many real-world applications (e.g. content matching or online labor markets), the knowledge about preferences may not be readily available and must be learned, i.e., one side of the market (aka agents) may not know their preferences over the other side (aka arms). Recent research on online settings has focused primarily on welfare optimization aspects (i.e. minimizing the overall regret) while paying little attention to the game-theoretic properties such as the stability of the final matching. In this paper, we exploit the structure of stable solutions to devise algorithms that improve the likelihood of finding stable solutions. We initiate the study of the sample complexity of finding a stable matching, and provide theoretical bounds on the number of samples needed to reach a stable matching with high probability. Finally, our empirical results demonstrate intriguing tradeoffs between stability and optimality of the proposed algorithms, further complementing our theoretical findings. | Putting Gale Shapley to Work: Guaranteeing Stability Through Learning | [
"Hadi Hosseini",
"Sanjukta Roy",
"Duohan Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=IUKff7nYmW | @inproceedings{
kovalev2024lower,
title={Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks},
author={Dmitry Kovalev and Ekaterina Borodich and Alexander Gasnikov and Dmitrii Feoktistov},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IUKff7nYmW}
} | We consider the task of minimizing the sum of convex functions stored in a decentralized manner across the nodes of a communication network. This problem is relatively well-studied in the scenario when the objective functions are smooth, or the links of the network are fixed in time, or both. In particular, lower bounds on the number of decentralized communications and (sub)gradient computations required to solve the problem have been established, along with matching optimal algorithms. However, the remaining and most challenging setting of non-smooth decentralized optimization over time-varying networks is largely underexplored, as neither lower bounds nor optimal algorithms are known in the literature. We resolve this fundamental gap with the following contributions: (i) we establish the first lower bounds on the communication and subgradient computation complexities of solving non-smooth convex decentralized optimization problems over time-varying networks; (ii) we develop the first optimal algorithm that matches these lower bounds and offers substantially improved theoretical performance compared to the existing state of the art. | Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks | [
"Dmitry Kovalev",
"Ekaterina Borodich",
"Alexander Gasnikov",
"Dmitrii Feoktistov"
] | NeurIPS.cc/2024/Conference | 2405.18031 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ISa7mMe7Vg | @inproceedings{
egashira2024exploiting,
title={Exploiting {LLM} Quantization},
author={Kazuki Egashira and Mark Vero and Robin Staab and Jingxuan He and Martin Vechev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ISa7mMe7Vg}
} | Quantization leverages lower-precision weights to reduce the memory usage of large language models (LLMs) and is a key technique for enabling their deployment on commodity hardware. While LLM quantization's impact on utility has been extensively explored, this work for the first time studies its adverse effects from a security perspective. We reveal that widely used quantization methods can be exploited to produce a harmful quantized LLM, even though the full-precision counterpart appears benign, potentially tricking users into deploying the malicious quantized model.
We demonstrate this threat using a three-staged attack framework: (i) first, we obtain a malicious LLM through fine-tuning on an adversarial task; (ii) next, we quantize the malicious model and calculate constraints that characterize all full-precision models that map to the same quantized model; (iii) finally, using projected gradient descent, we tune out the poisoned behavior from the full-precision model while ensuring that its weights satisfy the constraints computed in step (ii). This procedure results in an LLM that exhibits benign behavior in full precision but when quantized, it follows the adversarial behavior injected in step (i). We experimentally demonstrate the feasibility and severity of such an attack across three diverse scenarios: vulnerable code generation, content injection, and over-refusal attack. In practice, the adversary could host the resulting full-precision model on an LLM community hub such as Hugging Face, exposing millions of users to the threat of deploying its malicious quantized version on their devices. | Exploiting LLM Quantization | [
"Kazuki Egashira",
"Mark Vero",
"Robin Staab",
"Jingxuan He",
"Martin Vechev"
] | NeurIPS.cc/2024/Conference | 2405.18137 | [
"https://github.com/eth-sri/llm-quantization-attack"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IRXyPm9IPW | @inproceedings{
wu2024multimodal,
title={Multimodal Large Language Models Make Text-to-Image Generative Models Align Better},
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=IRXyPm9IPW}
} | Recent studies have demonstrated the exceptional potentials of leveraging human preference datasets to refine text-to-image generative models, enhancing the alignment between generated images and textual prompts. Despite these advances, current human preference datasets are either prohibitively expensive to construct or suffer from a lack of diversity in preference dimensions, resulting in limited applicability for instruction tuning in open-source text-to-image generative models and hinder further exploration. To address these challenges and promote the alignment of generative models through instruction tuning, we leverage multimodal large language models to create VisionPrefer, a high-quality and fine-grained preference dataset that captures multiple preference aspects. We aggregate feedback from AI annotators across four aspects: prompt-following, aesthetic, fidelity, and harmlessness to construct VisionPrefer. To validate the effectiveness of VisionPrefer, we train a reward model VP-Score over VisionPrefer to guide the training of text-to-image generative models and the preference prediction accuracy of VP-Score is comparable to human annotators. Furthermore, we use two reinforcement learning methods to supervised fine-tune generative models to evaluate the performance of VisionPrefer, and extensive experimental results demonstrate that VisionPrefer significantly improves text-image alignment in compositional image generation across diverse aspects, e.g., aesthetic, and generalizes better than previous human-preference metrics across various image distributions. Moreover, VisionPrefer indicates that the integration of AI-generated synthetic data as a supervisory signal is a promising avenue for achieving improved alignment with human preferences in vision generative models. | Multimodal Large Language Models Make Text-to-Image Generative Models Align Better | [
"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=IOKLUxB05h | @inproceedings{
hamilton2024combining,
title={Combining Observational Data and Language for Species Range Estimation},
author={Max Hamilton and Christian Lange and Elijah Cole and Alexander Shepard and Samuel Heinrich and Oisin Mac Aodha and Grant Van Horn and Subhransu Maji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IOKLUxB05h}
} | Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management. However, traditional SRMs rely on the availability of environmental covariates and high-quality observational data, both of which can be challenging to obtain due to geographic inaccessibility and resource constraints. We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia, covering habitat preferences and range descriptions for tens of thousands of species. Our framework maps location, species, and text descriptions into a common space, facilitating the learning of rich spatial covariates at a global scale and enabling zero-shot range estimation from textual descriptions. Evaluated on held-out species, our zero-shot SRMs significantly outperform baselines and match the performance of SRMs obtained using tens of observations. Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data. We present extensive quantitative and qualitative analyses of the learned representations in the context of range estimation and other spatial tasks, demonstrating the effectiveness of our approach. | Combining Observational Data and Language for Species Range Estimation | [
"Max Hamilton",
"Christian Lange",
"Elijah Cole",
"Alexander Shepard",
"Samuel Heinrich",
"Oisin Mac Aodha",
"Grant Van Horn",
"Subhransu Maji"
] | NeurIPS.cc/2024/Conference | 2410.10931 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=INAeUQ04lT | @inproceedings{
wang2024timexer,
title={TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables},
author={Yuxuan Wang and Haixu Wu and Jiaxiang Dong and Guo Qin and Haoran Zhang and Yong Liu and Yun-Zhong Qiu and Jianmin Wang and Mingsheng Long},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=INAeUQ04lT}
} | Deep models have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a system is often recorded into multiple variables, where the exogenous variables can provide valuable external information for endogenous variables. Thus, unlike well-established multivariate or univariate forecasting paradigms that either treat all the variables equally or ignore exogenous information, this paper focuses on a more practical setting: time series forecasting with exogenous variables. We propose a novel approach, TimeXer, to ingest external information to enhance the forecasting of endogenous variables. With deftly designed embedding layers, TimeXer empowers the canonical Transformer with the ability to reconcile endogenous and exogenous information, where patch-wise self-attention and variate-wise cross-attention are used simultaneously. Moreover, global endogenous tokens are learned to effectively bridge the causal information underlying exogenous series into endogenous temporal patches. Experimentally, TimeXer achieves consistent state-of-the-art performance on twelve real-world forecasting benchmarks and exhibits notable generality and scalability. Code is available at this repository: https://github.com/thuml/TimeXer. | TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables | [
"Yuxuan Wang",
"Haixu Wu",
"Jiaxiang Dong",
"Guo Qin",
"Haoran Zhang",
"Yong Liu",
"Yun-Zhong Qiu",
"Jianmin Wang",
"Mingsheng Long"
] | NeurIPS.cc/2024/Conference | 2402.19072 | [
"https://github.com/thuml/timexer"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IMlDpZmLnL | @inproceedings{
cheng2024a,
title={A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression},
author={Tin Sum Cheng and Aurelien Lucchi and Anastasis Kratsios and David Belius},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IMlDpZmLnL}
} | This paper conducts a comprehensive study of the learning curves of kernel ridge regression (KRR) under minimal assumptions.
Our contributions are three-fold: 1) we analyze the role of key properties of the kernel, such as its spectral eigen-decay, the characteristics of the eigenfunctions, and the smoothness of the kernel; 2) we demonstrate the validity of the Gaussian Equivalent Property (GEP), which states that the generalization performance of KRR remains the same when the whitened features are replaced by standard Gaussian vectors, thereby shedding light on the success of previous analyzes under the Gaussian Design Assumption; 3) we derive novel bounds that improve over existing bounds across a broad range of setting such as (in)dependent feature vectors and various combinations of eigen-decay rates in the over/underparameterized regimes. | A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression | [
"Tin Sum Cheng",
"Aurelien Lucchi",
"Anastasis Kratsios",
"David Belius"
] | NeurIPS.cc/2024/Conference | 2410.17796 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IM4LtYRWdE | @inproceedings{
albuquerque2024inflationary,
title={Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models},
author={Daniela F De Albuquerque and John Pearson},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IM4LtYRWdE}
} | Beyond estimating parameters of interest from data, one of the key goals of statistical inference is to properly quantify uncertainty in these estimates. In Bayesian inference, this uncertainty is provided by the posterior distribution, the computation of which typically involves an intractable high-dimensional integral. Among available approximation methods, sampling-based approaches come with strong theoretical guarantees but scale poorly to large problems, while variational approaches scale well but offer few theoretical guarantees. In particular, variational methods are known to produce overconfident estimates of posterior uncertainty and are typically non-identifiable, with many latent variable configurations generating equivalent predictions. Here, we address these challenges by showing how diffusion-based models (DBMs), which have recently produced state-of-the-art performance in generative modeling tasks, can be repurposed for performing calibrated, identifiable Bayesian inference. By exploiting a previously established connection between the stochastic and probability flow ordinary differential equations (pfODEs) underlying DBMs, we derive a class of models, \emph{inflationary flows,} that uniquely and deterministically map high-dimensional data to a lower-dimensional Gaussian distribution via ODE integration. This map is both invertible and neighborhood-preserving, with controllable numerical error, with the result that uncertainties in the data are correctly propagated to the latent space. We demonstrate how such maps can be learned via standard DBM training using a novel noise schedule and are effective at both preserving and reducing intrinsic data dimensionality. The result is a class of highly expressive generative models, uniquely defined on a low-dimensional latent space, that afford principled Bayesian inference. | Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models | [
"Daniela F De Albuquerque",
"John Pearson"
] | NeurIPS.cc/2024/Conference | 2407.08843 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IIoH8bf5BA | @inproceedings{
bertazzi2024piecewise,
title={Piecewise deterministic generative models},
author={Andrea Bertazzi and Dario Shariatian and Umut Simsekli and Eric Moulines and Alain Oliviero Durmus},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IIoH8bf5BA}
} | We introduce a novel class of generative models based on piecewise deterministic Markov processes (PDMPs), a family of non-diffusive stochastic processes consisting of deterministic motion and random jumps at random times. Similarly to diffusions, such Markov processes admit time reversals that turn out to be PDMPs as well. We apply this observation to three PDMPs considered in the literature: the Zig-Zag process, Bouncy Particle Sampler, and Randomised Hamiltonian Monte Carlo. For these three particular instances, we show that the jump rates and kernels of the corresponding time reversals admit explicit expressions depending on some conditional densities of the PDMP under consideration before and after a jump. Based on these results, we propose efficient training procedures to learn these characteristics and consider methods to approximately simulate the reverse process. Finally, we provide bounds in the total variation distance between the data distribution and the resulting distribution of our model in the case where the base distribution is the standard $d$-dimensional Gaussian distribution. Promising numerical simulations support further investigations into this class of models. | Piecewise deterministic generative models | [
"Andrea Bertazzi",
"Dario Shariatian",
"Umut Simsekli",
"Eric Moulines",
"Alain Oliviero Durmus"
] | NeurIPS.cc/2024/Conference | 2407.19448 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IHjoPnNZb9 | @inproceedings{
wen2024rethinking,
title={Rethinking Decoders for Transformer-based Semantic Segmentation: Compression is All You Need},
author={Qishuai Wen and Chun-Guang Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IHjoPnNZb9}
} | State-of-the-art methods for Transformer-based semantic segmentation typically adopt Transformer decoders that are used to extract additional embeddings from image embeddings via cross-attention, refine either or both types of embeddings via self-attention, and project image embeddings onto the additional embeddings via dot-product. Despite their remarkable success, these empirical designs still lack theoretical justifications or interpretations, thus hindering potentially principled improvements. In this paper, we argue that there are fundamental connections between semantic segmentation and compression, especially between the Transformer decoders and Principal Component Analysis (PCA). From such a perspective, we derive a white-box, fully attentional DEcoder for PrIncipled semantiC segemenTation (DEPICT), with the interpretations as follows: 1) the self-attention operator refines image embeddings to construct an ideal principal subspace that aligns with the supervision and retains most information; 2) the cross-attention operator seeks to find a low-rank approximation of the refined image embeddings, which is expected to be a set of orthonormal bases of the principal subspace and corresponds to the predefined classes; 3) the dot-product operation yields compact representation for image embeddings as segmentation masks. Experiments conducted on dataset ADE20K find that DEPICT consistently outperforms its black-box counterpart, Segmenter, and it is light weight and more robust. | Rethinking Decoders for Transformer-based Semantic Segmentation: Compression is All You Need | [
"Qishuai Wen",
"Chun-Guang Li"
] | NeurIPS.cc/2024/Conference | 2411.03033 | [
"https://github.com/qishuaiwen/depict"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IHjKpKljyH | @inproceedings{
schmitt2024consistency,
title={Consistency Models for Scalable and Fast Simulation-Based Inference},
author={Marvin Schmitt and Valentin Pratz and Ullrich Koethe and Paul-Christian B{\"u}rkner and Stefan T. Radev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IHjKpKljyH}
} | Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models.
In line with this goal, we present consistency models for posterior estimation (CMPE), a new conditional sampler for SBI that inherits the advantages of recent unconstrained architectures and overcomes their sampling inefficiency at inference time.
CMPE essentially distills a continuous probability flow and enables rapid few-shot inference with an unconstrained architecture that can be flexibly tailored to the structure of the estimation problem.
We provide hyperparameters and default architectures that support consistency training over a wide range of different dimensions, including low-dimensional ones which are important in SBI workflows but were previously difficult to tackle even with unconditional consistency models.
Our empirical evaluation demonstrates that CMPE not only outperforms current state-of-the-art algorithms on hard low-dimensional benchmarks, but also achieves competitive performance with much faster sampling speed on two realistic estimation problems with high data and/or parameter dimensions. | Consistency Models for Scalable and Fast Simulation-Based Inference | [
"Marvin Schmitt",
"Valentin Pratz",
"Ullrich Koethe",
"Paul-Christian Bürkner",
"Stefan T. Radev"
] | NeurIPS.cc/2024/Conference | 2312.05440 | [
"https://github.com/bayesflow-org/consistency-model-posterior-estimation"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IGn0ktYDwV | @inproceedings{
xie2024sampa,
title={{SAMP}a: Sharpness-aware Minimization Parallelized},
author={Wanyun Xie and Thomas Pethick and Volkan Cevher},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IGn0ktYDwV}
} | Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires _sequentially_ computing two gradients, effectively doubling the per-iteration cost compared to base optimizers like SGD. We propose a simple modification of SAM, termed SAMPa, which allows us to fully parallelize the two gradient computations. SAMPa achieves a twofold speedup of SAM under the assumption that communication costs between devices are negligible. Empirical results show that SAMPa ranks among the most efficient variants of SAM in terms of computational time. Additionally, our method consistently outperforms SAM across both vision and language tasks. Notably, SAMPa theoretically maintains convergence guarantees even for _fixed_ perturbation sizes, which is established through a novel Lyapunov function. We in fact arrive at SAMPa by treating this convergence guarantee as a hard requirement---an approach we believe is promising for developing SAM-based methods in general. Our code is available at https://github.com/LIONS-EPFL/SAMPa. | SAMPa: Sharpness-aware Minimization Parallelized | [
"Wanyun Xie",
"Thomas Pethick",
"Volkan Cevher"
] | NeurIPS.cc/2024/Conference | 2410.10683 | [
"https://github.com/lions-epfl/sampa"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IGhpUd496D | @inproceedings{
tao2024provable,
title={Provable Editing of Deep Neural Networks using Parametric Linear Relaxation},
author={Zhe Tao and Aditya Thakur},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IGhpUd496D}
} | Ensuring that a DNN satisfies a desired property is critical when deploying DNNs
in safety-critical applications. There are efficient methods that can verify
whether a DNN satisfies a property, as seen in the annual DNN verification
competition (VNN-COMP). However, the problem of provably editing a DNN to
satisfy a property remains challenging. We present PREPARED, the first efficient
technique for provable editing of DNNs. Given a DNN $\mathcal{N}$
with parameters $\theta$, input polytope $P$, and output
polytope $Q$, PREPARED finds new parameters $\theta'$ such that $\forall
\mathrm{x} \in P
. \mathcal{N}(\mathrm{x}; \theta') \in Q$
while minimizing the changes $\lVert{\theta' - \theta}\rVert$.
Given a DNN and a property it violates from the VNN-COMP benchmarks,
PREPARED is able to provably edit the DNN to satisfy this property within
45 seconds.
PREPARED is efficient
because it relaxes the NP-hard provable editing problem to solving a linear
program. The key contribution is the novel notion of Parametric Linear Relaxation,
which enables PREPARED to construct tight output bounds of the DNN that are parameterized
by the new parameters $\theta'$. We demonstrate that PREPARED is more efficient and
effective compared to prior DNN editing approaches i) using the VNN-COMP
benchmarks, ii) by editing CIFAR10 and TinyImageNet image-recognition DNNs, and
BERT sentiment-classification DNNs for local robustness, and iii) by training a
DNN to model a geodynamics process and satisfy physics constraints. | Provable Editing of Deep Neural Networks using Parametric Linear Relaxation | [
"Zhe Tao",
"Aditya Thakur"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=IGCaTQ4n1R | @inproceedings{
mao2024opendlign,
title={OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images},
author={Ye Mao and Junpeng Jing and Krystian Mikolajczyk},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IGCaTQ4n1R}
} | Recent open-world 3D representation learning methods using Vision-Language Models (VLMs) to align 3D point clouds with image-text information have shown superior 3D zero-shot performance. However, CAD-rendered images for this alignment often lack realism and texture variation, compromising alignment robustness. Moreover, the volume discrepancy between 3D and 2D pretraining datasets highlights the need for effective strategies to transfer the representational abilities of VLMs to 3D learning. In this paper, we present OpenDlign, a novel open-world 3D model using depth-aligned images generated from a diffusion model for robust multimodal alignment. These images exhibit greater texture diversity than CAD renderings due to the stochastic nature of the diffusion model. By refining the depth map projection pipeline and designing depth-specific prompts, OpenDlign leverages rich knowledge in pre-trained VLM for 3D representation learning with streamlined fine-tuning. Our experiments show that OpenDlign achieves high zero-shot and few-shot performance on diverse 3D tasks, despite only fine-tuning 6 million parameters on a limited ShapeNet dataset. In zero-shot classification, OpenDlign surpasses previous models by 8.0\% on ModelNet40 and 16.4\% on OmniObject3D. Additionally, using depth-aligned images for multimodal alignment consistently enhances the performance of other state-of-the-art models. | OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images | [
"Ye Mao",
"Junpeng Jing",
"Krystian Mikolajczyk"
] | NeurIPS.cc/2024/Conference | 2404.16538 | [
"https://github.com/Yebulabula/OpenDlign"
] | https://huggingface.co/papers/2404.16538 | 0 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=IGAN7RldcF | @inproceedings{
yao2024unveiling,
title={Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms},
author={Fan Yao and Yiming Liao and Jingzhou Liu and Shaoliang Nie and Qifan Wang and Haifeng Xu and Hongning Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IGAN7RldcF}
} | On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated user traffic. This phenomenon subtly shapes the volume and diversity of the content pool, which is crucial for the platform's sustainability. In this work, we demonstrate, both theoretically and empirically, that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. In contrast, a more aggressive exploration policy may slightly compromise user satisfaction but promote higher content creation volume. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on UGC platforms. Building on this finding, we propose an efficient optimization method to identify the optimal exploration strength, balancing user and creator engagement. Our model can serve as a pre-deployment audit tool for recommendation algorithms on UGC platforms, helping to align their immediate objectives with sustainable, long-term goals. | Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms | [
"Fan Yao",
"Yiming Liao",
"Jingzhou Liu",
"Shaoliang Nie",
"Qifan Wang",
"Haifeng Xu",
"Hongning Wang"
] | NeurIPS.cc/2024/Conference | 2410.23683 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IG6kd5V4kd | @inproceedings{
nguyen2024sigmoid,
title={Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts},
author={Huy Nguyen and Nhat Ho and Alessandro Rinaldo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IG6kd5V4kd}
} | The softmax gating function is arguably the most popular choice in mixture of experts modeling. Despite its widespread use in practice, the softmax gating may lead to unnecessary competition among experts, potentially causing the undesirable phenomenon of representation collapse due to its inherent structure. In response, the sigmoid gating function has been recently proposed as an alternative and has been demonstrated empirically to achieve superior performance. However, a rigorous examination of the sigmoid gating function is lacking in current literature. In this paper, we verify theoretically that the sigmoid gating, in fact, enjoys a higher sample efficiency than the softmax gating for the statistical task of expert estimation. Towards that goal, we consider a regression framework in which the unknown regression function is modeled as a mixture of experts, and study the rates of convergence of the least squares estimator under the over-specified case in which the number of fitted experts is larger than the true value. We show that two gating regimes naturally arise and, in each of them, we formulate an identifiability condition for the expert functions and derive the corresponding convergence rates. In both cases, we find that experts formulated as feed-forward networks with commonly used activation such as $\mathrm{ReLU}$ and $\mathrm{GELU}$ enjoy faster convergence rates under the sigmoid gating than those under softmax gating. Furthermore, given the same choice of experts, we demonstrate that the sigmoid gating function requires a smaller sample size than its softmax counterpart to attain the same error of expert estimation and, therefore, is more sample efficient. | Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts | [
"Huy Nguyen",
"Nhat Ho",
"Alessandro Rinaldo"
] | NeurIPS.cc/2024/Conference | 2405.13997 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IEyXWuXAQT | @inproceedings{
picard2024learning,
title={Learning via Surrogate {PAC}-Bayes},
author={Antoine Picard and Roman Moscoviz and Benjamin Guedj},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IEyXWuXAQT}
} | PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalisation bound. However, optimising generalisation bounds might not always be viable for tractable or computational reasons, or both. For example, iteratively querying the empirical risk might prove computationally expensive.
In response, we introduce a novel principled strategy for building an iterative learning algorithm via the optimisation of a sequence of surrogate training objectives, inherited from PAC-Bayes generalisation bounds. The key argument is to replace the empirical risk (seen as a function of hypotheses) in the generalisation bound by its projection onto a constructible low dimensional functional space: these projections can be queried much more efficiently than the initial risk. On top of providing that generic recipe for learning via surrogate PAC-Bayes bounds, we (i) contribute theoretical results establishing that iteratively optimising our surrogates implies the optimisation of the original generalisation bounds, (ii) instantiate this strategy to the framework of meta-learning, introducing a meta-objective offering a closed form expression for meta-gradient, (iii) illustrate our approach with numerical experiments inspired by an industrial biochemical problem. | Learning via Surrogate PAC-Bayes | [
"Antoine Picard",
"Roman Moscoviz",
"Benjamin Guedj"
] | NeurIPS.cc/2024/Conference | 2410.10230 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IDn9SiKgLy | @inproceedings{
xu2024principled,
title={Principled Bayesian Optimization in Collaboration with Human Experts},
author={Wenjie Xu and Masaki Adachi and Colin Jones and Michael A Osborne},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IDn9SiKgLy}
} | Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation process. We consider a setup where experts provide advice on the next query point through binary accept/reject recommendations (labels). Experts’ labels are often costly, requiring efficient use of their efforts, and can at the same time be unreliable, requiring careful adjustment of the degree to which any expert is trusted. We introduce the first principled approach that provides two key guarantees. (1) Handover guarantee: similar to a no-regret property, we establish a sublinear bound on the cumulative number of experts’ binary labels. Initially, multiple labels per query are needed, but the number of expert labels required asymptotically converges to zero, saving both expert effort and computation time. (2) No-harm guarantee with data-driven trust level adjustment: our adaptive trust level ensures that the convergence rate will not be worse than the one without using advice, even if the advice from experts is adversarial. Unlike existing methods that employ a user-defined function that hand-tunes the trust level adjustment, our approach enables data-driven adjustments. Real-world applications empirically demonstrate that our method not only outperforms existing baselines, but also maintains robustness despite varying labelling accuracy, in tasks of battery design with human experts. | Principled Bayesian Optimization in Collaboration with Human Experts | [
"Wenjie Xu",
"Masaki Adachi",
"Colin Jones",
"Michael A Osborne"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=IAse6CAG26 | @inproceedings{
chen2024tackling,
title={Tackling Uncertain Correspondences for Multi-Modal Entity Alignment},
author={Liyi Chen and Ying Sun and Shengzhe Zhang and Yuyang Ye and Wei Wu and Hui Xiong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IAse6CAG26}
} | Recently, multi-modal entity alignment has emerged as a pivotal endeavor for the integration of Multi-Modal Knowledge Graphs (MMKGs) originating from diverse data sources. Existing works primarily focus on fully depicting entity features by designing various modality encoders or fusion approaches. However, uncertain correspondences between inter-modal or intra-modal cues, such as weak inter-modal associations, description diversity, and modality absence, still severely hinder the effective exploration of aligned entity similarities. To this end, in this paper, we propose a novel Tackling uncertain correspondences method for Multi-modal Entity Alignment (TMEA). Specifically, to handle diverse attribute knowledge descriptions, we design alignment-augmented abstract representation that incorporates the large language model and in-context learning into attribute alignment and filtering for generating and embedding the attribute abstract. In order to mitigate the influence of the modality absence, we propose to unify all modality features into a shared latent subspace and generate pseudo features via variational autoencoders according to existing modal features. Then, we develop an inter-modal commonality enhancement mechanism based on cross-attention with orthogonal constraints, to address weak semantic associations between modalities. Extensive experiments on two real-world datasets validate the effectiveness of TMEA with a clear improvement over competitive baselines. | Tackling Uncertain Correspondences for Multi-Modal Entity Alignment | [
"Liyi Chen",
"Ying Sun",
"Shengzhe Zhang",
"Yuyang Ye",
"Wei Wu",
"Hui Xiong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=IAQNJUJe8q | @inproceedings{
kong2024fullatom,
title={Full-Atom Peptide Design with Geometric Latent Diffusion},
author={Xiangzhe Kong and Yinjun Jia and Wenbing Huang and Yang Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IAQNJUJe8q}
} | Peptide design plays a pivotal role in therapeutics, allowing brand new possibility to leverage target binding sites that are previously undruggable. Most existing methods are either inefficient or only concerned with the target-agnostic design of 1D sequences. In this paper, we propose a generative model for full-atom Peptide design with Geometric LAtent Diffusion (PepGLAD) given the binding site. We first establish a benchmark consisting of both 1D sequences and 3D structures from Protein Data Bank (PDB) and literature for systematic evaluation. We then identify two major challenges of leveraging current diffusion-based models for peptide design: the full-atom geometry and the variable binding geometry. To tackle the first challenge, PepGLAD derives a variational autoencoder that first encodes full-atom residues of variable size into fixed-dimensional latent representations, and then decodes back to the residue space after conducting the diffusion process in the latent space. For the second issue, PepGLAD explores a receptor-specific affine transformation to convert the 3D coordinates into a shared standard space, enabling better generalization ability across different binding shapes. Experimental Results show that our method not only improves diversity and binding affinity significantly in the task of sequence-structure co-design, but also excels at recovering reference structures for binding conformation generation. | Full-Atom Peptide Design with Geometric Latent Diffusion | [
"Xiangzhe Kong",
"Yinjun Jia",
"Wenbing Huang",
"Yang Liu"
] | NeurIPS.cc/2024/Conference | 2402.13555 | [
"https://github.com/thunlp-mt/pepglad"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IAAPhOLhcX | @inproceedings{
zhang2024how,
title={How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective},
author={Qiaozhe Zhang and Ruijie ZHANG and Jun Sun and Yingzhuang Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IAAPhOLhcX}
} | Network pruning is a commonly used measure to alleviate the storage and computational burden of deep neural networks. However, the fundamental limit of network pruning is still lacking. To close the gap, in this work we'll take a first-principles approach, i.e. we'll directly impose the sparsity constraint on the loss function and leverage the framework of *statistical dimension* in convex geometry, thus we're able to characterize the sharp phase transition point, i.e. the fundamental limit of the pruning ratio. Through this fundamental limit, we're able to identify two key factors that determine the pruning ratio limit, namely, *weight magnitude* and *network flatness*. Generally speaking, the flatter the loss landscape or the smaller the weight magnitude, the smaller pruning ratio. Moreover, we provide efficient countermeasures to address the challenges in the computation of the pruning limit, which involves accurate spectrum estimation of a large-scale and non-positive Hessian matrix. Moreover, through the lens of the pruning ratio threshold, we can provide rigorous interpretations on several heuristics in existing pruning algorithms. Extensive experiments are performed that demonstrate that our theoretical pruning ratio threshold coincides very well with the experiments. All codes are available at: https://anonymous.4open.science/r/Global-One-shot-Pruning-BC7B | How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective | [
"Qiaozhe Zhang",
"Ruijie ZHANG",
"Jun Sun",
"Yingzhuang Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=I96GFYalFO | @inproceedings{
tan2024fedssp,
title={Fed{SSP}: Federated Graph Learning with Spectral Knowledge and Personalized Preference},
author={Zihan Tan and Guancheng Wan and Wenke Huang and Mang Ye},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I96GFYalFO}
} | Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework $\textbf{FedSSP}$ which $\textbf{S}$hares generic $\textbf{S}$pectral knowledge while satisfying graph $\textbf{P}$references. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://github.com/OakleyTan/FedSSP. | FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference | [
"Zihan Tan",
"Guancheng Wan",
"Wenke Huang",
"Mang Ye"
] | NeurIPS.cc/2024/Conference | 2410.20105 | [
"https://github.com/oakleytan/fedssp"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=I90ypQpLgL | @inproceedings{
bachoc2024fair,
title={Fair Online Bilateral Trade},
author={Fran{\c{c}}ois Bachoc and Nicol{\`o} Cesa-Bianchi and Tommaso Cesari and Roberto Colomboni},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I90ypQpLgL}
} | In online bilateral trade, a platform posts prices to incoming pairs of buyers and sellers that have private valuations for a certain good. If the price is lower than the buyers' valuation and higher than the sellers' valuation, then a trade takes place. Previous work focused on the platform perspective, with the goal of setting prices maximizing the *gain from trade* (the sum of sellers' and buyers' utilities). Gain from trade is, however, potentially unfair to traders, as they may receive highly uneven shares of the total utility. In this work we enforce fairness by rewarding the platform with the _fair gain from trade_, defined as the minimum between sellers' and buyers' utilities.
After showing that any no-regret learning algorithm designed to maximize the sum of the utilities may fail badly with fair gain from trade, we present our main contribution: a complete characterization of the regret regimes for fair gain from trade when, after each interaction, the platform only learns whether each trader accepted the current price. Specifically, we prove the following regret bounds: $\Theta(\ln T)$ in the deterministic setting, $\Omega(T)$ in the stochastic setting, and $\tilde{\Theta}(T^{2/3})$ in the stochastic setting when sellers' and buyers' valuations are independent of each other. We conclude by providing tight regret bounds when, after each interaction, the platform is allowed to observe the true traders' valuations. | Fair Online Bilateral Trade | [
"François Bachoc",
"Nicolò Cesa-Bianchi",
"Tommaso Cesari",
"Roberto Colomboni"
] | NeurIPS.cc/2024/Conference | 2405.13919 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=I8PkICj9kM | @inproceedings{
mcallister2024rethinking,
title={Rethinking Score Distillation as a Bridge Between Image Distributions},
author={David McAllister and Songwei Ge and Jia-Bin Huang and David W. Jacobs and Alexei A Efros and Aleksander Holynski and Angjoo Kanazawa},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I8PkICj9kM}
} | Score distillation sampling (SDS) has proven to be an important tool, enabling the use of large-scale diffusion priors for tasks operating in data-poor domains. Unfortunately, SDS has a number of characteristic artifacts that limit its utility in general-purpose applications. In this paper, we make progress toward understanding the behavior of SDS and its variants by viewing them as solving an optimal-cost transport path from some current source distribution to a target distribution. Under this new interpretation, we argue that these methods' characteristic artifacts are caused by (1) linear approximation of the optimal path and (2) poor estimates of the source distribution.
We show that by calibrating the text conditioning of the source distribution, we can produce high-quality generation and translation results with little extra overhead. Our method can be easily applied across many domains, matching or beating the performance of specialized methods. We demonstrate its utility in text-to-2D, text-to-3D, translating paintings to real images, optical illusion generation, and 3D sketch-to-real. We compare our method to existing approaches for score distillation sampling and show that it can produce high-frequency details with realistic colors. | Rethinking Score Distillation as a Bridge Between Image Distributions | [
"David McAllister",
"Songwei Ge",
"Jia-Bin Huang",
"David W. Jacobs",
"Alexei A Efros",
"Aleksander Holynski",
"Angjoo Kanazawa"
] | NeurIPS.cc/2024/Conference | 2406.09417 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=I6tRENM5Ya | @inproceedings{
mo2024revisiting,
title={Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective},
author={Yujie Mo and Zhihe Lu and Runpeng Yu and Xiaofeng Zhu and Xinchao Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I6tRENM5Ya}
} | Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clustering approaches, they encounter two significant limitations: (i) noise in graph structures is often introduced during the message-passing process to weaken node representations, and (ii) cluster-level information may be inadequately captured and leveraged, diminishing the performance in downstream tasks. In this paper, we address these limitations by theoretically revisiting SHGL from the spectral clustering perspective and introducing a novel framework enhanced by rank and dual consistency constraints. Specifically, our framework incorporates a rank-constrained spectral clustering method that refines the affinity matrix to exclude noise effectively. Additionally, we integrate node-level and cluster-level consistency constraints that concurrently capture invariant and clustering information to facilitate learning in downstream tasks. We theoretically demonstrate that the learned representations are divided into distinct partitions based on the number of classes and exhibit enhanced generalization ability across tasks. Experimental results affirm the superiority of our method, showcasing remarkable improvements in several downstream tasks compared to existing methods. | Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective | [
"Yujie Mo",
"Zhihe Lu",
"Runpeng Yu",
"Xiaofeng Zhu",
"Xinchao Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=I6tBNcJE2F | @inproceedings{
fang2024realworld,
title={Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network},
author={Chengyu Fang and Chunming He and Fengyang Xiao and Yulun Zhang and Longxiang Tang and Yuelin Zhang and Kai Li and Xiu Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I6tBNcJE2F}
} | Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real haze distributions and the scarcity of paired real-world data. To address these challenges, we first introduce a cooperative unfolding network that jointly models atmospheric scattering and image scenes, effectively integrating physical knowledge into deep networks to restore haze-contaminated details. Additionally, we propose the first RID-oriented iterative mean-teacher framework, termed the Coherence-based Label Generator, to generate high-quality pseudo labels for network training. Specifically, we provide an optimal label pool to store the best pseudo-labels during network training, leveraging both global and local coherence to select high-quality candidates and assign weights to prioritize haze-free regions. We verify the effectiveness of our method, with experiments demonstrating that it achieves state-of-the-art performance on RID tasks. Code will be available at https://github.com/cnyvfang/CORUN-Colabator. | Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network | [
"Chengyu Fang",
"Chunming He",
"Fengyang Xiao",
"Yulun Zhang",
"Longxiang Tang",
"Yuelin Zhang",
"Kai Li",
"Xiu Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=I3IuclVLFZ | @inproceedings{
liu2024fedlpa,
title={Fed{LPA}: One-shot Federated Learning with Layer-Wise Posterior Aggregation},
author={Xiang Liu and Liangxi Liu and Feiyang Ye and Yunheng Shen and Xia Li and Linshan Jiang and Jialin Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I3IuclVLFZ}
} | Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning. Recently, motivated by diminishing privacy concerns, mitigating potential attacks, and reducing communication overhead, one-shot federated learning (i.e., limiting client-server communication into a single round) has gained popularity among researchers. However, the one-shot aggregation performances are sensitively affected by the non-identical training data distribution, which exhibits high statistical heterogeneity in some real-world scenarios. To address this issue, we propose a novel one-shot aggregation method with layer-wise posterior aggregation, named FedLPA. FedLPA aggregates local models to obtain a more accurate global model without requiring extra auxiliary datasets or exposing any private label information, e.g., label distributions. To effectively capture the statistics maintained in the biased local datasets in the practical non-IID scenario, we efficiently infer the posteriors of each layer in each local model using layer-wise Laplace approximation and aggregate them to train the global parameters. Extensive experimental results demonstrate that FedLPA significantly improves learning performance over state-of-the-art methods across several metrics. | FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation | [
"Xiang Liu",
"Liangxi Liu",
"Feiyang Ye",
"Yunheng Shen",
"Xia Li",
"Linshan Jiang",
"Jialin Li"
] | NeurIPS.cc/2024/Conference | 2310.00339 | [
"https://github.com/lebronlambert/FedLPA_NeurIPS2024"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=I2gVmVRgNk | @inproceedings{
zeng2024towards,
title={Towards Understanding Evolving Patterns in Sequential Data},
author={QIUHAO Zeng and Long-Kai Huang and Qi CHEN and Charles Ling and Boyu Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I2gVmVRgNk}
} | In many machine learning tasks, data is inherently sequential. Most existing algorithms learn from sequential data in an auto-regressive manner, which predicts the next unseen data point based on the observed sequence, implicitly assuming the presence of an \emph{evolving pattern} embedded in the data that can be leveraged. However, identifying and assessing evolving patterns in learning tasks often relies on subjective judgments rooted in the prior knowledge of human experts, lacking a standardized quantitative measure. Furthermore, such measures enable us to determine the suitability of employing sequential models effectively and make informed decisions on the temporal order of time series data, and feature/data selection processes. To address this issue, we introduce the Evolving Rate (EvoRate), which quantitatively approximates the intensity of evolving patterns in the data with Mutual Information. Furthermore, in some temporal data with neural mutual information estimations, we only have snapshots at different timestamps, lacking correspondence, which hinders EvoRate estimation. To tackle this challenge, we propose EvoRate$_\mathcal{W}$, aiming to establish correspondence with optimal transport for estimating the first-order EvoRate. Experiments on synthetic and real-world datasets including images and tabular data validate the efficacy of our EvoRate. | Towards Understanding Evolving Patterns in Sequential Data | [
"QIUHAO Zeng",
"Long-Kai Huang",
"Qi CHEN",
"Charles Ling",
"Boyu Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=I29aiMdm4u | @inproceedings{
kwan2024nvrc,
title={{NVRC}: Neural Video Representation Compression},
author={Ho Man Kwan and Ge Gao and Fan Zhang and Andrew Peter Gower and David Bull},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I29aiMdm4u}
} | Recent advances in implicit neural representation (INR)-based video coding have
demonstrated its potential to compete with both conventional and other learning-
based approaches. With INR methods, a neural network is trained to overfit a
video sequence, with its parameters compressed to obtain a compact representation
of the video content. However, although promising results have been achieved,
the best INR-based methods are still out-performed by the latest standard codecs,
such as VVC VTM, partially due to the simple model compression techniques
employed. In this paper, rather than focusing on representation architectures, which
is a common focus in many existing works, we propose a novel INR-based video
compression framework, Neural Video Representation Compression (NVRC),
targeting compression of the representation. Based on its novel quantization and
entropy coding approaches, NVRC is the first framework capable of optimizing an
INR-based video representation in a fully end-to-end manner for the rate-distortion
trade-off. To further minimize the additional bitrate overhead introduced by the
entropy models, NVRC also compresses all the network, quantization and entropy
model parameters hierarchically. Our experiments show that NVRC outperforms
many conventional and learning-based benchmark codecs, with a 23% average
coding gain over VVC VTM (Random Access) on the UVG dataset, measured
in PSNR. As far as we are aware, this is the first time an INR-based video codec
achieving such performance. | NVRC: Neural Video Representation Compression | [
"Ho Man Kwan",
"Ge Gao",
"Fan Zhang",
"Andrew Peter Gower",
"David Bull"
] | NeurIPS.cc/2024/Conference | 2409.07414 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HzANl2unCB | @inproceedings{
sun2024chattracker,
title={ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model},
author={Yiming Sun and Fan Yu and Shaoxiang Chen and Yu Zhang and Junwei Huang and Yang Li and Chenhui Li and Changbo Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HzANl2unCB}
} | Visual object tracking aims to locate a targeted object in a video sequence based on an initial bounding box. Recently, Vision-Language~(VL) trackers have proposed to utilize additional natural language descriptions to enhance versatility in various applications. However, VL trackers are still inferior to State-of-The-Art (SoTA) visual trackers in terms of tracking performance. We found that this inferiority primarily results from their heavy reliance on manual textual annotations, which include the frequent provision of ambiguous language descriptions. In this paper, we propose ChatTracker to leverage the wealth of world knowledge in the Multimodal Large Language Model (MLLM) to generate high-quality language descriptions and enhance tracking performance. To this end, we propose a novel reflection-based prompt optimization module to iteratively refine the ambiguous and inaccurate descriptions of the target with tracking feedback. To further utilize semantic information produced by MLLM, a simple yet effective VL tracking framework is proposed and can be easily integrated as a plug-and-play module to boost the performance of both VL and visual trackers. Experimental results show that our proposed ChatTracker achieves a performance comparable to existing methods. | ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model | [
"Yiming Sun",
"Fan Yu",
"Shaoxiang Chen",
"Yu Zhang",
"Junwei Huang",
"Yang Li",
"Chenhui Li",
"Changbo Wang"
] | NeurIPS.cc/2024/Conference | 2411.01756 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Hz6cSigMyU | @inproceedings{
wen2024reinforcing,
title={Reinforcing {LLM} Agents via Policy Optimization with Action Decomposition},
author={Muning Wen and Ziyu Wan and Jun Wang and Weinan Zhang and Ying Wen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Hz6cSigMyU}
} | Language models as intelligent agents push the boundaries of sequential decision-making agents but struggle with limited knowledge of environmental dynamics and exponentially huge action space. Recent efforts like GLAM and TWOSOME manually constrain the action space to a restricted subset and employ reinforcement learning to align agents' knowledge with specific environments. However, they overlook fine-grained credit assignments for intra-action tokens, which is essential for efficient language agent optimization, and rely on human's prior knowledge to restrict action space. This paper proposes decomposing language agent optimization from the action level to the token level, offering finer supervision for each intra-action token and manageable optimization complexity in environments with unrestricted action spaces. Beginning with the simplification of flattening all actions, we theoretically explore the discrepancies between action-level optimization and this naive token-level optimization. We then derive the Bellman backup with Action Decomposition (BAD) to integrate credit assignments for both intra-action and inter-action tokens, effectively eliminating the discrepancies. Implementing BAD within the PPO algorithm, we introduce Policy Optimization with Action Decomposition (POAD). POAD benefits from a finer-grained credit assignment process and lower optimization complexity, leading to enhanced learning efficiency and generalization abilities in aligning language agents with interactive environments. We validate POAD across diverse testbeds, with results affirming the advantages of our approach and the correctness of our theoretical analysis. The source code can be accessed directly with this link: https://github.com/morning9393/ADRL. | Reinforcing LLM Agents via Policy Optimization with Action Decomposition | [
"Muning Wen",
"Ziyu Wan",
"Jun Wang",
"Weinan Zhang",
"Ying Wen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HyxjSi3SzF | @inproceedings{
jia2024communication,
title={Communication Bounds for the Distributed Experts Problem},
author={Zhihao Jia and Qi Pang and Trung Tran and David Woodruff and Zhihao Zhang and Wenting Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HyxjSi3SzF}
} | In this work, we study the experts problem in the distributed setting where an expert's cost needs to be aggregated across multiple servers. Our study considers various communication models such as the message-passing model and the broadcast model, along with multiple aggregation functions, such as summing and taking the $\ell_p$ norm of an expert's cost across servers. We propose the first communication-efficient protocols that achieve near-optimal regret in these settings, even against a strong adversary who can choose the inputs adaptively. Additionally, we give a conditional lower bound showing that the communication of our protocols is nearly optimal. Finally, we implement our protocols and demonstrate empirical savings on the HPO-B benchmarks. | Communication Bounds for the Distributed Experts Problem | [
"Zhihao Jia",
"Qi Pang",
"Trung Tran",
"David Woodruff",
"Zhihao Zhang",
"Wenting Zheng"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HxGdbAmYYr | @inproceedings{
yang2024mixture,
title={Mixture of Adversarial Lo{RA}s: Boosting Robust Generalization in Meta-Tuning},
author={Xu Yang and Chen Liu and Ying Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HxGdbAmYYr}
} | This paper introduces AMT, an \textbf{A}dversarial \textbf{M}eta-\textbf{T}uning methodology, to boost the robust generalization of pre-trained models in the out-of-domain (OOD) few-shot learning. To address the challenge of transferring knowledge from source domains to unseen target domains, we construct the robust LoRAPool by meta-tuning LoRAs with dual perturbations applied to not only the inputs but also singular values and vectors of the weight matrices at various robustness levels. On top of that, we introduce a simple yet effective test-time merging mechanism to dynamically merge discriminative LoRAs for test-time task customization. Extensive evaluations demonstrate that AMT yields significant improvements, up to 12.92\% in clean generalization and up to 49.72\% in adversarial generalization, over previous state-of-the-art methods across a diverse range of OOD few-shot image classification tasks on three benchmarks, confirming the effectiveness of our approach to boost the robust generalization of pre-trained models. Our code is available at \href{https://github.com/xyang583/AMT}{https://github.com/xyang583/AMT}. | Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-Tuning | [
"Xu Yang",
"Chen Liu",
"Ying Wei"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HwO1mNluoL | @inproceedings{
basu2024mitigating,
title={Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks},
author={Abhipsa Basu and Saswat Subhajyoti Mallick and Venkatesh Babu Radhakrishnan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HwO1mNluoL}
} | In image classification, it is common to utilize a pretrained model to extract meaningful features of the input images, and then to train a classifier on top of it to make predictions for any downstream task. Trained on enormous amounts of data, these models have been shown to contain harmful biases which can hurt their performance when adapted for a downstream classification task. Further, very often they may be blackbox, either due to scale, or because of unavailability of model weights or architecture. Thus, during a downstream task, we cannot debias such models by updating the weights of the feature encoder, as only the classifier can be finetuned. In this regard, we investigate the suitability of some existing debiasing techniques and thereby motivate the need for more focused research towards this problem setting. Furthermore, we propose a simple method consisting of a clustering-based adaptive margin loss with a blackbox feature encoder, with no knowledge of the bias attribute. Our experiments demonstrate the effectiveness of our method across multiple benchmarks. | Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks | [
"Abhipsa Basu",
"Saswat Subhajyoti Mallick",
"Venkatesh Babu Radhakrishnan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HvCppnDykt | @inproceedings{
kotekal2024variance,
title={Variance estimation in compound decision theory under boundedness},
author={Subhodh Kotekal},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HvCppnDykt}
} | The normal means model is often studied under the assumption of a known variance. However, ignorance of the variance is a frequent issue in applications and basic theoretical questions still remain open in this setting. This article establishes that the sharp minimax rate of variance estimation in square error is $(\frac{\log\log n}{\log n})^2$ under arguably the most mild assumption imposed for identifiability: bounded means. The rate-optimal estimator proposed in this article achieves the optimal rate by estimating $O\left(\frac{\log n}{\log\log n}\right)$ cumulants and leveraging a variational representation of the noise variance in terms of the cumulants of the data distribution. The minimax lower bound involves a moment matching construction. | Variance estimation in compound decision theory under boundedness | [
"Subhodh Kotekal"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HtlfNbyfOn | @inproceedings{
liu2024bitbit,
title={bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction},
author={Yehe Liu and Alexander Krull and Hector Basevi and Ales Leonardis and Michael W. Jenkins},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HtlfNbyfOn}
} | Quanta image sensors, such as SPAD arrays, are an emerging sensor technology, producing 1-bit arrays representing photon detection events over exposures as short as a few nanoseconds. In practice, raw data are post-processed using heavy spatiotemporal binning to create more useful and interpretable images at the cost of degrading spatiotemporal resolution. In this work, we propose bit2bit, a new method for reconstructing high-quality image stacks at the original spatiotemporal resolution from sparse binary quanta image data. Inspired by recent work on Poisson denoising, we developed an algorithm that creates a dense image sequence from sparse binary photon data by predicting the photon arrival location probability distribution. However, due to the binary nature of the data, we show that the assumption of a Poisson distribution is inadequate. Instead, we model the process with a Bernoulli lattice process from the truncated Poisson. This leads to the proposal of a novel self-supervised solution based on a masked loss function. We evaluate our method using both simulated and real data. On simulated data from a conventional video, we achieve 34.35 mean PSNR with extremely photon-sparse binary input (<0.06 photons per pixel per frame). We also present a novel dataset containing a wide range of real SPAD high-speed videos under various challenging imaging conditions. The scenes cover strong/weak ambient light, strong motion, ultra-fast events, etc., which will be made available to the community, on which we demonstrate the promise of our approach. Both reconstruction quality and throughput substantially surpass the state-of-the-art methods (e.g., Quanta Burst Photography (QBP)). Our approach significantly enhances the visualization and usability of the data, enabling the application of existing analysis techniques. | bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction | [
"Yehe Liu",
"Alexander Krull",
"Hector Basevi",
"Ales Leonardis",
"Michael W. Jenkins"
] | NeurIPS.cc/2024/Conference | 2410.23247 | [
"https://github.com/lyehe/ssunet"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HpN4xeDJQF | @inproceedings{
wang2024beyond,
title={Beyond Single Stationary Policies: Meta-Task Players as Naturally Superior Collaborators},
author={Haoming Wang and Zhaoming Tian and Yunpeng Song and Xiangliang Zhang and Zhongmin Cai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HpN4xeDJQF}
} | In human-AI collaborative tasks, the distribution of human behavior, influenced by mental models, is non-stationary, manifesting in various levels of initiative and different collaborative strategies. A significant challenge in human-AI collaboration is determining how to collaborate effectively with humans exhibiting non-stationary dynamics. Current collaborative agents involve initially running self-play (SP) multiple times to build a policy pool, followed by training the final adaptive policy against this pool. These agents themselves are a single policy network, which is $\textbf{insufficient for handling non-stationary human dynamics}$. We discern that despite the inherent diversity in human behaviors, the $\textbf{underlying meta-tasks within specific collaborative contexts tend to be strikingly similar}$. Accordingly, we propose a $\textbf{C}$ollaborative $\textbf{B}$ayesian $\textbf{P}$olicy $\textbf{R}$euse ($\textbf{CBPR}$), a novel Bayesian-based framework that $\textbf{adaptively selects optimal collaborative policies matching the current meta-task from multiple policy networks}$ instead of just selecting actions relying on a single policy network. We provide theoretical guarantees for CBPR's rapid convergence to the optimal policy once human partners alter their policies. This framework shifts from directly modeling human behavior to identifying various meta-tasks that support human decision-making and training meta-task playing (MTP) agents tailored to enhance collaboration. Our method undergoes rigorous testing in a well-recognized collaborative cooking simulator, $\textit{Overcooked}$. Both empirical results and user studies demonstrate CBPR's superior competitiveness compared to existing baselines. | Beyond Single Stationary Policies: Meta-Task Players as Naturally Superior Collaborators | [
"Haoming Wang",
"Zhaoming Tian",
"Yunpeng Song",
"Xiangliang Zhang",
"Zhongmin Cai"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HmMSBhMAw4 | @inproceedings{
sinha2024periodic,
title={Periodic agent-state based Q-learning for {POMDP}s},
author={Amit Sinha and Matthieu Geist and Aditya Mahajan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HmMSBhMAw4}
} | The standard approach for Partially Observable Markov Decision Processes (POMDPs) is to convert them to a fully observed belief-state MDP. However, the belief state depends on the system model and is therefore not viable in reinforcement learning (RL) settings. A widely used alternative is to use an agent state, which is a model-free, recursively updateable function of the observation history. Examples include frame stacking and recurrent neural networks. Since the agent state is model-free, it is used to adapt standard RL algorithms to POMDPs. However, standard RL algorithms like Q-learning learn a stationary policy. Our main thesis that we illustrate via examples is that because the agent state does not satisfy the Markov property, non-stationary agent-state based policies can outperform stationary ones. To leverage this feature, we propose PASQL (periodic agent-state based Q-learning), which is a variant of agent-state-based Q-learning that learns periodic policies. By combining ideas from periodic Markov chains and stochastic approximation, we rigorously establish that PASQL converges to a cyclic limit and characterize the approximation error of the converged periodic policy. Finally, we present a numerical experiment to highlight the salient features of PASQL and demonstrate the benefit of learning periodic policies over stationary policies. | Periodic agent-state based Q-learning for POMDPs | [
"Amit Sinha",
"Matthieu Geist",
"Aditya Mahajan"
] | NeurIPS.cc/2024/Conference | 2407.06121 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HmCmxbCpp2 | @inproceedings{
yin2024sgnav,
title={{SG}-Nav: Online 3D Scene Graph Prompting for {LLM}-based Zero-shot Object Navigation},
author={Hang Yin and Xiuwei Xu and Zhenyu Wu and Jie Zhou and Jiwen Lu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HmCmxbCpp2}
} | In this paper, we propose a new framework for zero-shot object navigation.
Existing zero-shot object navigation methods prompt LLM with the text of spatially closed objects, which lacks enough scene context for in-depth reasoning.
To better preserve the information of environment and fully exploit the reasoning ability of LLM, we propose to represent the observed scene with 3D scene graph. The scene graph encodes the relationships between objects, groups and rooms with a LLM-friendly structure, for which we design a hierarchical chain-of-thought prompt to help LLM reason the goal location according to scene context by traversing the nodes and edges.
Moreover, benefit from the scene graph representation, we further design a re-perception mechanism to empower the object navigation framework with the ability to correct perception error.
We conduct extensive experiments on MP3D, HM3D and RoboTHOR environments, where SG-Nav surpasses previous state-of-the-art zero-shot methods by more than \textbf{10\%} SR on all benchmarks, while the decision process is explainable. To the best of our knowledge, SG-Nav is the first zero-shot method that achieves even higher performance than supervised object navigation methods on the challenging MP3D benchmark.
Code of this project will be released in the final version. | SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation | [
"Hang Yin",
"Xiuwei Xu",
"Zhenyu Wu",
"Jie Zhou",
"Jiwen Lu"
] | NeurIPS.cc/2024/Conference | 2410.08189 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Hlcek7AYgP | @inproceedings{
chen2024neural,
title={Neural Embeddings Rank: Aligning 3D latent dynamics with movements},
author={Chenggang Chen and Zhiyu Yang and Xiaoqin Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Hlcek7AYgP}
} | Aligning neural dynamics with movements is a fundamental goal in neuroscience and brain-machine interfaces. However, there is still a lack of dimensionality reduction methods that can effectively align low-dimensional latent dynamics with movements. To address this gap, we propose Neural Embeddings Rank (NER), a technique that embeds neural dynamics into a 3D latent space and contrasts the embeddings based on movement ranks. NER learns to regress continuous representations of neural dynamics (i.e., embeddings) on continuous movements. We apply NER and six other dimensionality reduction techniques to neurons in the primary motor cortex (M1), dorsal premotor cortex (PMd), and primary somatosensory cortex (S1) as monkeys perform reaching tasks. Only NER aligns latent dynamics with both hand position and direction, visualizable in 3D. NER reveals consistent latent dynamics in M1 and PMd across sixteen sessions over a year. Using a linear regression decoder, NER explains 86\% and 97\% of the variance in velocity and position, respectively. Linear models trained on data from one session successfully decode velocity, position, and direction in held-out test data from different dates and cortical areas (64\%, 88\%, and 90\%). NER also reveals distinct latent dynamics in S1 during consistent movements and in M1 during curved reaching tasks. The code is available at https://github.com/NeuroscienceAI/NER. | Neural Embeddings Rank: Aligning 3D latent dynamics with movements | [
"Chenggang Chen",
"Zhiyu Yang",
"Xiaoqin Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HkMCCFrYkT | @inproceedings{
cai2024hdrgs,
title={{HDR}-{GS}: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting},
author={Yuanhao Cai and Zihao Xiao and Yixun Liang and Minghan Qin and Yulun Zhang and Xiaokang Yang and Yaoyao Liu and Alan Yuille},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HkMCCFrYkT}
} | High dynamic range (HDR) novel view synthesis (NVS) aims to create photorealistic images from novel viewpoints using HDR imaging techniques. The rendered HDR images capture a wider range of brightness levels containing more details of the scene than normal low dynamic range (LDR) images. Existing HDR NVS methods are mainly based on NeRF. They suffer from long training time and slow inference speed. In this paper, we propose a new framework, High Dynamic Range Gaussian Splatting (HDR-GS), which can efficiently render novel HDR views and reconstruct LDR images with a user input exposure time. Specifically, we design a Dual Dynamic Range (DDR) Gaussian point cloud model that uses spherical harmonics to fit HDR color and employs an MLP-based tone-mapper to render LDR color. The HDR and LDR colors are then fed into two Parallel Differentiable Rasterization (PDR) processes to reconstruct HDR and LDR views. To establish the data foundation for the research of 3D Gaussian splatting-based methods in HDR NVS, we recalibrate the camera parameters and compute the initial positions for Gaussian point clouds. Comprehensive experiments show that HDR-GS surpasses the state-of-the-art NeRF-based method by 3.84 and 1.91 dB on LDR and HDR NVS while enjoying 1000$\times$ inference speed and only costing 6.3\% training time. Code and data are released at https://github.com/caiyuanhao1998/HDR-GS | HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting | [
"Yuanhao Cai",
"Zihao Xiao",
"Yixun Liang",
"Minghan Qin",
"Yulun Zhang",
"Xiaokang Yang",
"Yaoyao Liu",
"Alan Yuille"
] | NeurIPS.cc/2024/Conference | 2405.15125 | [
"https://github.com/caiyuanhao1998/hdr-gs"
] | https://huggingface.co/papers/2405.15125 | 5 | 5 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=HkC4OYee3Q | @inproceedings{
rathbun2024sleepernets,
title={SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents},
author={Ethan Rathbun and Christopher Amato and Alina Oprea},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HkC4OYee3Q}
} | Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications -- making it paramount to ensure the robustness of RL algorithms against adversarial attacks. In this work we explore a particularly stealthy form of training-time attacks against RL -- backdoor poisoning. Here the adversary intercepts the training of an RL agent with the goal of reliably inducing a particular action when the agent observes a pre-determined trigger at inference time. We uncover theoretical limitations of prior work by proving their inability to generalize across domains and MDPs. Motivated by this, we formulate a novel poisoning attack framework which interlinks the adversary's objectives with those of finding an optimal policy -- guaranteeing attack success in the limit. Using insights from our theoretical analysis we develop "SleeperNets" as a universal backdoor attack which exploits a newly proposed threat model and leverages dynamic reward poisoning techniques. We evaluate our attack in 6 environments spanning multiple domains and demonstrate significant improvements in attack success over existing methods, while preserving benign episodic return. | SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents | [
"Ethan Rathbun",
"Christopher Amato",
"Alina Oprea"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HjeKHxK2VH | @inproceedings{
giboulot2024watermax,
title={WaterMax: breaking the {LLM} watermark detectability-robustness-quality trade-off},
author={Eva Giboulot and Teddy Furon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HjeKHxK2VH}
} | Watermarking is a technical means to dissuade malfeasant usage of Large Language Models.
This paper proposes a novel watermarking scheme, so-called WaterMax, that enjoys high detectability while sustaining the quality of the generated text of the original LLM.
Its new design leaves the LLM untouched (no modification of the weights, logits or temperature).
WaterMax balances robustness and computational complexity contrary to the watermarking techniques of the literature inherently provoking a trade-off between quality and robustness.
Its performance is both theoretically proven and experimentally validated.
It outperforms all the SotA techniques under the most complete benchmark suite. | WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off | [
"Eva Giboulot",
"Teddy Furon"
] | NeurIPS.cc/2024/Conference | 2403.04808 | [
"https://github.com/eva-giboulot/watermax"
] | https://huggingface.co/papers/2403.04808 | 1 | 0 | 0 | 2 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=HhnpPISAUH | @inproceedings{
chen2024heterogeneityguided,
title={Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-{IID} Federated Learning},
author={Huancheng Chen and Haris Vikalo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HhnpPISAUH}
} | Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult. Particularly challenging are the settings where due to communication resource constraints only a small fraction of clients can participate in any given round of FL. Recent approaches to training a global model in FL systems with non-IID data have focused on developing client selection methods that aim to sample clients with more informative updates of the model. However, existing client selection techniques either introduce significant computation overhead or perform well only in the scenarios where clients have data with similar heterogeneity profiles. In this paper, we propose HiCS-FL (Federated Learning via Hierarchical Clustered Sampling), a novel client selection method in which the server estimates statistical heterogeneity of a client's data using the client’s update of the network’s output layer and relies on this information to cluster and sample the clients. We analyze the ability of the proposed techniques to compare heterogeneity of different datasets, and characterize convergence of the training process that deploys the introduced client selection method. Extensive experimental results demonstrate that in non-IID settings HiCS-FL achieves faster convergence than state-of-the-art FL client selection schemes. Notably, HiCS-FL drastically reduces computation cost compared to existing selection schemes and is adaptable to different heterogeneity scenarios. | Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning | [
"Huancheng Chen",
"Haris Vikalo"
] | NeurIPS.cc/2024/Conference | 2310.00198 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Hgqs1b4ECy | @inproceedings{
chen2024inferring,
title={Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors},
author={Chao Chen and Yu-Shen Liu and Zhizhong Han},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Hgqs1b4ECy}
} | It is important to estimate an accurate signed distance function (SDF) from a point cloud in many computer vision applications. The latest methods learn neural SDFs using either a data-driven based or an overfitting-based strategy. However, these two kinds of methods are with either poor generalization or slow convergence, which limits their capability under challenging scenarios like highly noisy point clouds. To resolve this issue, we propose a method to prompt pros of both data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs. We introduce a novel statistical reasoning algorithm in local regions which is able to finetune data-driven based priors without signed distance supervision, clean point cloud, or point normals. This helps our method start with a good initialization, and converge to a minimum in a much faster way. Our numerical and visual comparisons with the stat-of-the-art methods show our superiority over these methods in surface reconstruction and point cloud denoising on widely used shape and scene benchmarks. The code is available at https://github.com/chenchao15/LocalN2NM. | Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors | [
"Chao Chen",
"Yu-Shen Liu",
"Zhizhong Han"
] | NeurIPS.cc/2024/Conference | 2410.19680 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HfztZgwpxI | @inproceedings{
dapueto2024transferring,
title={Transferring disentangled representations: bridging the gap between synthetic and real images},
author={Jacopo Dapueto and Nicoletta Noceti and Francesca Odone},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HfztZgwpxI}
} | Developing meaningful and efficient representations that separate the fundamental structure of the data generation mechanism is crucial in representation learning. However, Disentangled Representation Learning has not fully shown its potential on real images, because of correlated generative factors, their resolution and limited access to ground truth labels. Specifically on the latter, we investigate the possibility of leveraging synthetic data to learn general-purpose disentangled representations applicable to real data, discussing the effect of fine-tuning and what properties of disentanglement are preserved after the transfer. We provide an extensive empirical study to address these issues. In addition, we propose a new interpretable intervention-based metric, to measure the quality of factors encoding in the representation. Our results indicate that some level of disentanglement, transferring a representation from synthetic to real data, is possible and effective. | Transferring disentangled representations: bridging the gap between synthetic and real images | [
"Jacopo Dapueto",
"Nicoletta Noceti",
"Francesca Odone"
] | NeurIPS.cc/2024/Conference | 2409.18017 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HfpV6u0kbX | @inproceedings{
xia2024efficient,
title={Efficient Multi-task {LLM} Quantization and Serving for Multiple Lo{RA} Adapters},
author={Yifei Xia and Fangcheng Fu and Wentao Zhang and Jiawei Jiang and Bin CUI},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HfpV6u0kbX}
} | With the remarkable achievements of large language models (LLMs), the demand for fine-tuning and deploying LLMs in various downstream tasks has garnered widespread interest. Parameter-efficient fine-tuning techniques represented by LoRA and model quantization techniques represented by GPTQ and AWQ are of paramount significance. However, although these techniques have been widely adopted in single-task scenarios, research is scarce in multi-task scenarios. To be specific, we find that mainstream quantization methods would prevent the base LLM from being shared among tasks, so current LLM serving systems are infeasible to integrate LLM quantization with multiple LoRA adapters to achieve memory-efficient multi-task serving. Moreover, existing LLM serving systems lack support for dynamic task addition and overlook the workload differences among tasks, leading to inefficiencies in multi-task scenarios.
This work proposes LoRA-Inlaid, an efficient multi-task LLM serving system. On the one hand, LoRA-Inlaid designs a flexible and efficient multi-task quantization algorithm (MLGPTQ) that facilitates the sharing of a single quantized model for multiple LoRA adapters, which significantly reduces the memory consumption for model deployment. Meanwhile, it supports adding LoRA adapters for new tasks on the fly, without sacrificing the stability of online services. On the other hand, LoRA-Inlaid develops a novel multi-task scheduling algorithm guided by output length prediction and grouping among different tasks, which effectively shrinks the memory consumption and avoids frequent switching of LoRA adapters. Empirical results verify that LoRA-Inlaid outperforms existing state-of-the-art LLM serving systems by up to 1.58 times in terms of throughput, 1.76 times in terms of average latency, 2 times in terms of job completion time, and 10 times in terms of SLO Attainment, while maintaining the same level of model quality. | Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters | [
"Yifei Xia",
"Fangcheng Fu",
"Wentao Zhang",
"Jiawei Jiang",
"Bin CUI"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HfSJlBRkKJ | @inproceedings{
chihaoui2024blind,
title={Blind Image Restoration via Fast Diffusion Inversion},
author={Hamadi Chihaoui and Abdelhak Lemkhenter and Paolo Favaro},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HfSJlBRkKJ}
} | Image Restoration (IR) methods based on a pre-trained diffusion model have demonstrated state-of-the-art performance. However, they have two fundamental limitations: 1) they often assume that the degradation operator is completely known and 2) they alter the diffusion sampling process, which may result in restored images that do not lie onto the data manifold. To address these issues, we propose Blind Image Restoration via fast Diffusion inversion (BIRD) a blind IR method that jointly optimizes for the degradation model parameters and the restored image. To ensure that the restored images lie onto the data manifold, we propose a novel sampling technique on a pre-trained diffusion model. A key idea in our method is not to modify the reverse sampling, i.e., not to alter all the intermediate latents, once an initial noise is sampled. This is ultimately equivalent to casting the IR task as an optimization problem in the space of the input noise. Moreover, to mitigate the computational cost associated with inverting a fully unrolled diffusion model, we leverage the inherent capability of these models to skip ahead in the forward diffusion process using large time steps. We experimentally validate BIRD on several image restoration tasks and show that it achieves state of the art performance. | Blind Image Restoration via Fast Diffusion Inversion | [
"Hamadi Chihaoui",
"Abdelhak Lemkhenter",
"Paolo Favaro"
] | NeurIPS.cc/2024/Conference | 2405.19572 | [
"https://github.com/hamadichihaoui/BIRD"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HfQF8LoLhs | @inproceedings{
bertholom2024asymptotics,
title={Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families},
author={Fran{\c{c}}ois Bertholom and randal douc and Fran{\c{c}}ois Roueff},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HfQF8LoLhs}
} | Recent works in Variational Inference have examined alternative criteria to the commonly used exclusive Kullback-Leibler divergence. Encouraging empirical results have been obtained with the family of alpha-divergences, but few works have focused on the asymptotic properties of the proposed algorithms, especially as the number of iterations goes to infinity. In this paper, we study a procedure that ensures a monotonic decrease in the alpha-divergence. We provide sufficient conditions to guarantee its convergence to a local minimizer of the alpha-divergence at a geometric rate when the variational family belongs to the class of exponential models. The sample-based version of this ideal procedure involves biased gradient estimators, thus hindering any theoretical study. We propose an alternative unbiased algorithm, we prove its almost sure convergence to a local minimizer of the alpha-divergence, and a law of the iterated logarithm. Our results are exemplified with toy and real-data experiments. | Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families | [
"François Bertholom",
"randal douc",
"François Roueff"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Hew2JSDycr | @inproceedings{
guo2024biscope,
title={BiScope: {AI}-generated Text Detection by Checking Memorization of Preceding Tokens},
author={Hanxi Guo and Siyuan Cheng and Xiaolong Jin and ZHUO ZHANG and Kaiyuan Zhang and Guanhong Tao and Guangyu Shen and Xiangyu Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Hew2JSDycr}
} | Detecting text generated by Large Language Models (LLMs) is a pressing need in
order to identify and prevent misuse of these powerful models in a wide range of
applications, which have highly undesirable consequences such as misinformation
and academic dishonesty. Given a piece of subject text, many existing detection
methods work by measuring the difficulty of LLM predicting the next token in
the text from their prefix. In this paper, we make a critical observation that
how well the current token’s output logits memorizes the closely preceding input
tokens also provides strong evidence. Therefore, we propose a novel bi-directional
calculation method that measures the cross-entropy losses between an output
logits and the ground-truth token (forward) and between the output logits and
the immediately preceding input token (backward). A classifier is trained to
make the final prediction based on the statistics of these losses. We evaluate our
system, named BISCOPE, on texts generated by five latest commercial LLMs
across five heterogeneous datasets, including both natural language and code.
BISCOPE demonstrates superior detection accuracy and robustness compared to six
existing baseline methods, exceeding the state-of-the-art non-commercial methods’
detection accuracy by over 0.30 F1 score, achieving over 0.95 detection F1 score
on average. It also outperforms the best commercial tool GPTZero that is based on
a commercial LLM trained with an enormous volume of data. Code is available at https://github.com/MarkGHX/BiScope. | BiScope: AI-generated Text Detection by Checking Memorization of Preceding Tokens | [
"Hanxi Guo",
"Siyuan Cheng",
"Xiaolong Jin",
"ZHUO ZHANG",
"Kaiyuan Zhang",
"Guanhong Tao",
"Guangyu Shen",
"Xiangyu Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HeoRsnaD44 | @inproceedings{
ayadi2024unified,
title={Unified Guidance for Geometry-Conditioned Molecular Generation},
author={Sirine Ayadi and Leon Hetzel and Johanna Sommer and Fabian J Theis and Stephan G{\"u}nnemann},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HeoRsnaD44}
} | Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios. | Unified Guidance for Geometry-Conditioned Molecular Generation | [
"Sirine Ayadi",
"Leon Hetzel",
"Johanna Sommer",
"Fabian J Theis",
"Stephan Günnemann"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HeJ1cBAgiV | @inproceedings{
mangold2024scafflsa,
title={{SCAFFLSA}: Taming Heterogeneity in Federated Linear Stochastic Approximation and {TD} Learning},
author={Paul Mangold and Sergey Samsonov and Safwan Labbi and Ilya Levin and Reda ALAMI and Alexey Naumov and Eric Moulines},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HeJ1cBAgiV}
} | In this paper, we analyze the sample and communication complexity of the federated linear stochastic approximation (FedLSA) algorithm. We explicitly quantify the effects of local training with agent heterogeneity. We show that the communication complexity of FedLSA scales polynomially with the inverse of the desired accuracy ϵ. To overcome this, we propose SCAFFLSA a new variant of FedLSA that uses control variates to correct for client drift, and establish its sample and communication complexities. We show that for statistically heterogeneous agents, its communication complexity scales logarithmically with the desired accuracy, similar to Scaffnew. An important finding is that, compared to the existing results for Scaffnew, the sample complexity scales with the inverse of the number of agents, a property referred to as linear speed-up. Achieving this linear speed-up requires completely new theoretical arguments. We apply the proposed method to federated temporal difference learning with linear function approximation and analyze the corresponding complexity improvements. | SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning | [
"Paul Mangold",
"Sergey Samsonov",
"Safwan Labbi",
"Ilya Levin",
"Reda ALAMI",
"Alexey Naumov",
"Eric Moulines"
] | NeurIPS.cc/2024/Conference | 2402.04114 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=He2GCHeRML | @inproceedings{
subedi2024empowering,
title={Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism},
author={Ronast Subedi and Lu Wei and Wenhan Gao and Shayok Chakraborty and Yi Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=He2GCHeRML}
} | Molecular learning is pivotal in many real-world applications, such as drug discovery. Supervised learning requires heavy human annotation, which is particularly challenging for molecular data, e.g., the commonly used density functional theory (DFT) is highly computationally expensive. Active learning (AL) automatically queries labels for most informative samples, thereby remarkably alleviating the annotation hurdle. In this paper, we present a principled AL paradigm for molecular learning, where we treat molecules as 3D molecular graphs. Specifically, we propose a new diversity sampling method to eliminate mutual redundancy built on distributions of 3D geometries. We first propose a set of new 3D graph isometries for 3D graph isomorphism analysis. Our method is provably at least as expressive as the Geometric Weisfeiler-Lehman (GWL) test. The moments of the distributions of the associated geometries are then extracted for efficient diversity computing. To ensure our AL paradigm selects samples with maximal uncertainties, we carefully design a Bayesian geometric graph neural network to compute uncertainties specifically for 3D molecular graphs. We pose active sampling as a quadratic programming (QP) problem using the proposed components. Experimental results demonstrate the effectiveness of our AL paradigm, as well as the proposed diversity and uncertainty methods. | Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism | [
"Ronast Subedi",
"Lu Wei",
"Wenhan Gao",
"Shayok Chakraborty",
"Yi Liu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Hd2EOwKItm | @inproceedings{
huang2024classification,
title={Classification Done Right for Vision-Language Pre-Training},
author={Zilong Huang and Qinghao Ye and Bingyi Kang and Jiashi Feng and Haoqi Fan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Hd2EOwKItm}
} | We introduce SuperClass, a super simple classification method for vision-language pre-training on image-text data. Unlike its contrastive counterpart CLIP who contrast with a text encoder, SuperClass directly utilizes tokenized raw text as supervised classification labels, without the need for additional text filtering or selection. Due to the absence of the text encoding as contrastive target, SuperClass does not require a text encoder and does not need to maintain a large batch size as CLIP does. SuperClass demonstrated superior performance on various downstream tasks, including classic computer vision benchmarks and vision language downstream tasks. We further explored the scaling behavior of SuperClass on model size, training length, or data size, and reported encouraging results and comparisons to CLIP. https://github.com/x-cls/superclass | Classification Done Right for Vision-Language Pre-Training | [
"Zilong Huang",
"Qinghao Ye",
"Bingyi Kang",
"Jiashi Feng",
"Haoqi Fan"
] | NeurIPS.cc/2024/Conference | 2411.03313 | [
"https://github.com/x-cls/superclass"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HcqnhqoXS3 | @inproceedings{
zheng2024decomposed,
title={Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization},
author={Hongling Zheng and Li Shen and Yong Luo and Tongliang Liu and Jialie Shen and Dacheng Tao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HcqnhqoXS3}
} | Multi-task offline reinforcement learning aims to develop a unified policy for diverse tasks without requiring real-time interaction with the environment. Recent work explores sequence modeling, leveraging the scalability of the transformer architecture as a foundation for multi-task learning. Given the variations in task content and complexity, formulating policies becomes a challenging endeavor, requiring careful parameter sharing and adept management of conflicting gradients to extract rich cross-task knowledge from multiple tasks and transfer it to unseen tasks. In this paper, we propose the Decomposed Prompt Decision Transformer (DPDT) that adopts a two-stage paradigm to efficiently learn prompts for unseen tasks in a parameter-efficient manner. We incorporate parameters from pre-trained language models (PLMs) to initialize DPDT, thereby providing rich prior knowledge encoded in language models. During the decomposed prompt tuning phase, we learn both cross-task and task-specific prompts on training tasks to achieve prompt decomposition. In the test time adaptation phase, the cross-task prompt, serving as a good initialization, were further optimized on unseen tasks through test time adaptation, enhancing the model's performance on these tasks. Empirical evaluation on a series of Meta-RL benchmarks demonstrates the superiority of our approach. The project is available at https://github.com/ruthless-man/DPDT. | Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization | [
"Hongling Zheng",
"Li Shen",
"Yong Luo",
"Tongliang Liu",
"Jialie Shen",
"Dacheng Tao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HcqV2bPFKz | @inproceedings{
miao2024hierarchical,
title={Hierarchical Object-Aware Dual-Level Contrastive Learning for Domain Generalized Stereo Matching},
author={Yikun Miao and Meiqing Wu and Siew Kei Lam and Changsheng Li and Thambipillai Srikanthan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HcqV2bPFKz}
} | Stereo matching algorithms that leverage end-to-end convolutional neural networks have recently demonstrated notable advancements in performance. However, a common issue is their susceptibility to domain shifts, hindering their ability in generalizing to diverse, unseen realistic domains. We argue that existing stereo matching networks overlook the importance of extracting semantically and structurally meaningful features. To address this gap, we propose an effective hierarchical object-aware dual-level contrastive learning (HODC) framework for domain generalized stereo matching. Our framework guides the model in extracting features that support semantically and structurally driven matching by segmenting objects at different scales and enhances correspondence between intra- and inter-scale regions from the left feature map to the right using dual-level contrastive loss. HODC can be integrated with existing stereo matching models in the training stage, requiring no modifications to the architecture. Remarkably, using only synthetic datasets for training, HODC achieves state-of-the-art generalization performance with various existing stereo matching network architectures, across multiple realistic datasets. | Hierarchical Object-Aware Dual-Level Contrastive Learning for Domain Generalized Stereo Matching | [
"Yikun Miao",
"Meiqing Wu",
"Siew Kei Lam",
"Changsheng Li",
"Thambipillai Srikanthan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HcifdQZFZV | @inproceedings{
hsu2024safe,
title={Safe Lo{RA}: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models},
author={Chia-Yi Hsu and Yu-Lin Tsai and Chih-Hsun Lin and Pin-Yu Chen and Chia-Mu Yu and Chun-Ying Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HcifdQZFZV}
} | While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized datasets, domain-specific tasks, or other private needs. However, fine-tuning all parameters of LLMs requires significant hardware resources, which can be impractical for typical users. Therefore, parameter-efficient fine-tuning such as LoRA have emerged, allowing users to fine-tune LLMs without the need for considerable computing resources, with little performance degradation compared to fine-tuning all parameters. Unfortunately, recent studies indicate that fine-tuning can increase the risk to the safety of LLMs, even when data does not contain malicious content. To address this challenge, we propose $\textsf{Safe LoRA}$, a simple one-liner patch to the original LoRA implementation by introducing the projection of LoRA weights from selected layers to the safety-aligned subspace, effectively reducing the safety risks in LLM fine-tuning while maintaining utility. It is worth noting that $\textsf{Safe LoRA}$ is a training-free and data-free approach, as it only requires the knowledge of the weights from the base and aligned LLMs. Our extensive experiments demonstrate that when fine-tuning on purely malicious data, $\textsf{Safe LoRA}$ retains similar safety performance as the original aligned model. Moreover, when the fine-tuning dataset contains a mixture of both benign and malicious data, $\textsf{Safe LoRA}$ mitigates the negative effect made by malicious data while preserving performance on downstream tasks. Our codes are available at https://github.com/IBM/SafeLoRA. | Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models | [
"Chia-Yi Hsu",
"Yu-Lin Tsai",
"Chih-Hsun Lin",
"Pin-Yu Chen",
"Chia-Mu Yu",
"Chun-Ying Huang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HbV5vRJMOY | @inproceedings{
jain2024mixture,
title={Mixture of Nested Experts: Adaptive Processing of Visual Tokens},
author={Gagan Jain and Nidhi Hegde and Aditya Kusupati and Arsha Nagrani and Shyamal Buch and Prateek Jain and Anurag Arnab and Sujoy Paul},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HbV5vRJMOY}
} | The visual medium (images and videos) naturally contains a large amount of information redundancy, thereby providing a great opportunity for leveraging efficiency in processing. While Vision Transformer (ViT) based models scale effectively to large data regimes, they fail to capitalize on this inherent redundancy, leading to higher computational costs. Mixture of Experts (MoE) networks demonstrate scalability while maintaining same inference-time costs, but they come with a larger parameter footprint. We present Mixture of Nested Experts (MoNE), which utilizes a nested structure for experts, wherein individual experts fall on an increasing compute-accuracy curve. Given a compute budget, MoNE learns to dynamically choose tokens in a priority order, and thus redundant tokens are processed through cheaper nested experts. Using this framework, we achieve equivalent performance as the baseline models, while reducing inference time compute by over two-fold. We validate our approach on standard image and video datasets - ImageNet-21K, Kinetics400, and Something-Something-v2. We further highlight MoNE's adaptability by showcasing its ability to maintain strong performance across different inference-time compute budgets on videos, using only a single trained model. | Mixture of Nested Experts: Adaptive Processing of Visual Tokens | [
"Gagan Jain",
"Nidhi Hegde",
"Aditya Kusupati",
"Arsha Nagrani",
"Shyamal Buch",
"Prateek Jain",
"Anurag Arnab",
"Sujoy Paul"
] | NeurIPS.cc/2024/Conference | 2407.19985 | [
""
] | https://huggingface.co/papers/2407.19985 | 6 | 35 | 3 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=HbIBqn3grD | @inproceedings{
costacurta2024structured,
title={Structured flexibility in recurrent neural networks via neuromodulation},
author={Julia C Costacurta and Shaunak Bhandarkar and David M. Zoltowski and Scott Linderman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HbIBqn3grD}
} | A core aim in theoretical and systems neuroscience is to develop models which help us better understand biological intelligence.
Such models range broadly in both complexity and biological plausibility.
One widely-adopted example is task-optimized recurrent neural networks (RNNs), which have been used to generate hypotheses about how the brain’s neural dynamics may organize to accomplish tasks.
However, task-optimized RNNs typically have a fixed weight matrix representing the synaptic connectivity between neurons. From decades of neuroscience research, we know that synaptic weights are constantly changing, controlled in part by chemicals such as neuromodulators.
In this work we explore the computational implications of synaptic gain scaling, a form of neuromodulation, using task-optimized low-rank RNNs.
In our neuromodulated RNN (NM-RNN) model, a neuromodulatory subnetwork outputs a low-dimensional neuromodulatory signal that dynamically scales the low-rank recurrent weights of an output-generating RNN.
In empirical experiments, we find that the structured flexibility in the NM-RNN allows it to both train and generalize with a higher degree of accuracy than low-rank RNNs on a set of canonical tasks.
Additionally, via theoretical analyses we show how neuromodulatory gain scaling endows networks with gating mechanisms commonly found in artificial RNNs.
We end by analyzing the low-rank dynamics of trained NM-RNNs, to show how task computations are distributed. | Structured flexibility in recurrent neural networks via neuromodulation | [
"Julia C Costacurta",
"Shaunak Bhandarkar",
"David M. Zoltowski",
"Scott Linderman"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=HavKlV22xJ | @inproceedings{
stojanovic2024modelfree,
title={Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation},
author={Stefan Stojanovic and Yassir Jedra and Alexandre Proutiere},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HavKlV22xJ}
} | We consider the problem of learning an $\varepsilon$-optimal policy in controlled dynamical systems with low-rank latent structure.
For this problem, we present LoRa-PI (Low-Rank Policy Iteration), a model-free learning algorithm alternating between policy improvement and policy evaluation steps. In the latter, the algorithm estimates the low-rank matrix corresponding to the (state, action) value function of the current policy using the following two-phase procedure. The entries of the matrix are first sampled uniformly at random to estimate, via a spectral method, the *leverage scores* of its rows and columns. These scores are then used to extract a few important rows and columns whose entries are further sampled. The algorithm exploits these new samples to complete the matrix estimation using a CUR-like method. For this leveraged matrix estimation procedure, we establish entry-wise guarantees that remarkably, do not depend on the coherence of the matrix but only on its spikiness. These guarantees imply that LoRa-PI learns an $\varepsilon$-optimal policy using $\tilde{\cal O}({(S+A)\over \mathrm{poly}(1-\gamma)\varepsilon^2})$ samples where $S$ (resp. $A$) denotes the number of states (resp. actions) and $\gamma$ the discount factor. Our algorithm achieves this order-optimal (in $S$, $A$ and $\varepsilon$) sample complexity under milder conditions than those assumed in previously proposed approaches. | Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation | [
"Stefan Stojanovic",
"Yassir Jedra",
"Alexandre Proutiere"
] | NeurIPS.cc/2024/Conference | 2410.23434 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=HYiR6tGQPv | @inproceedings{
qin2024a,
title={A probability contrastive learning framework for 3D molecular representation learning},
author={Jiayu Qin and Jian Chen and Rohan Sharma and Jingchen Sun and Changyou Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=HYiR6tGQPv}
} | Contrastive Learning (CL) plays a crucial role in molecular representation learning, enabling unsupervised learning from large scale unlabeled molecule datasets. It has inspired various applications in molecular property prediction and drug design.
However, existing molecular representation learning methods often introduce potential false positive and false negative pairs through conventional graph augmentations like node masking and subgraph removal. The issue can lead to suboptimal performance when applying standard contrastive learning techniques to molecular datasets.
To address the issue of false positive and negative pairs in molecular representation learning, we propose a novel probability-based contrastive learning (CL) framework. Unlike conventional methods, our approach introduces a learnable weight distribution via Bayesian modeling to automatically identify and mitigate false positive and negative pairs. This method is particularly effective because it dynamically adjusts to the data, improving the accuracy of the learned representations. Our model is learned by a stochastic expectation-maximization process, which optimizes the model by iteratively refining the probability estimates of sample weights and updating the model parameters.
Experimental results indicate that our method outperforms existing approaches in 13 out of 15 molecular property prediction benchmarks in MoleculeNet dataset and 8 out of 12 benchmarks in the QM9 benchmark, achieving new state-of-the-art results on average. | A probability contrastive learning framework for 3D molecular representation learning | [
"Jiayu Qin",
"Jian Chen",
"Rohan Sharma",
"Jingchen Sun",
"Changyou Chen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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