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null | https://openreview.net/forum?id=iAkhPz7Qt3 | @inproceedings{
shao2024scaling,
title={Scaling Retrieval-Based Language Models with a Trillion-Token Datastore},
author={Rulin Shao and Jacqueline He and Akari Asai and Weijia Shi and Tim Dettmers and Sewon Min and Luke Zettlemoyer and Pang Wei Koh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=iAkhPz7Qt3}
} | Scaling laws with respect to the amount of training data and the number of parameters allow us to predict the cost-benefit trade-offs of pretraining language models (LMs) in different configurations. In this paper, we consider another dimension of scaling: the amount of data available at inference time. Specifically, we find that increasing the size of the datastore used by a retrieval-based LM monotonically improves language modeling and several downstream tasks without obvious saturation, such that a smaller model augmented with a large datastore outperforms a larger LM-only model on knowledge-intensive tasks. By plotting compute-optimal scaling curves with varied datastore, model, and pretraining data sizes, we show that using larger datastores can significantly improve model performance for the same training compute budget. We carry out our study by constructing a 1.4 trillion-token datastore named MassiveDS, which is the largest and the most diverse open-sourced datastore for retrieval-based LMs to date, and designing an efficient pipeline for studying datastore scaling in an accessible manner. Finally, we analyze the effect of improving the retriever, datastore quality filtering, and other design choices on our observed scaling trends. Overall, our results show that datastore size should be considered as an integral part of LM efficiency and performance trade-offs. To facilitate future research, we open-source our datastore and code at https://github.com/RulinShao/retrieval-scaling. | Scaling Retrieval-Based Language Models with a Trillion-Token Datastore | [
"Rulin Shao",
"Jacqueline He",
"Akari Asai",
"Weijia Shi",
"Tim Dettmers",
"Sewon Min",
"Luke Zettlemoyer",
"Pang Wei Koh"
] | NeurIPS.cc/2024/Conference | 2407.12854 | [
"https://github.com/rulinshao/retrieval-scaling"
] | https://huggingface.co/papers/2407.12854 | 2 | 29 | 2 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=i9QpRjUAhv | @inproceedings{
bocheng2024hico,
title={HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation},
author={Bocheng and YuhangMa and wuliebucha and Shanyuan Liu and Ao Ma and Xiaoyu Wu and Dawei Leng and Yuhui Yin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i9QpRjUAhv}
} | The task of layout-to-image generation involves synthesizing images based on the captions of objects and their spatial positions. Existing methods still struggle in complex layout generation, where common bad cases include object missing, inconsistent lighting, conflicting view angles, etc. To effectively address these issues, we propose a \textbf{Hi}erarchical \textbf{Co}ntrollable (HiCo) diffusion model for layout-to-image generation, featuring object seperable conditioning branch structure. Our key insight is to achieve spatial disentanglement through hierarchical modeling of layouts. We use a multi branch structure to represent hierarchy and aggregate them in fusion module. To evaluate the performance of multi-objective controllable layout generation in natural scenes, we introduce the HiCo-7K benchmark, derived from the GRIT-20M dataset and manually cleaned. https://github.com/360CVGroup/HiCo_T2I. | HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation | [
"Bocheng",
"YuhangMa",
"wuliebucha",
"Shanyuan Liu",
"Ao Ma",
"Xiaoyu Wu",
"Dawei Leng",
"Yuhui Yin"
] | NeurIPS.cc/2024/Conference | 2410.14324 | [
"https://github.com/360cvgroup/hico_t2i"
] | https://huggingface.co/papers/2410.14324 | 0 | 0 | 0 | 8 | [
"qihoo360/HiCo_T2I"
] | [] | [
"qihoo360/HiCo_T2I"
] | [
"qihoo360/HiCo_T2I"
] | [] | [
"qihoo360/HiCo_T2I"
] | 1 | poster |
null | https://openreview.net/forum?id=i8LoWBJf7j | @inproceedings{
do2024interpretable,
title={Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors},
author={VIET HO TAM THUC DO and Parham Eftekhar and Seyed Alireza Hosseini and Gene Cheung and Philip Chou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i8LoWBJf7j}
} | We build interpretable and lightweight transformer-like neural networks by unrolling iterative optimization algorithms that minimize graph smoothness priors---the quadratic graph Laplacian regularizer (GLR) and the $\ell_1$-norm graph total variation (GTV)---subject to an interpolation constraint. The crucial insight is that a normalized signal-dependent graph learning module amounts to a variant of the basic self-attention mechanism in conventional transformers. Unlike "black-box" transformers that require learning of large key, query and value matrices to compute scaled dot products as affinities and subsequent output embeddings, resulting in huge parameter sets, our unrolled networks employ shallow CNNs to learn low-dimensional features per node to establish pairwise Mahalanobis distances and construct sparse similarity graphs. At each layer, given a learned graph, the target interpolated signal is simply a low-pass filtered output derived from the minimization of an assumed graph smoothness prior, leading to a dramatic reduction in parameter count. Experiments for two image interpolation applications verify the restoration performance, parameter efficiency and robustness to covariate shift of our graph-based unrolled networks compared to conventional transformers. | Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors | [
"VIET HO TAM THUC DO",
"Parham Eftekhar",
"Seyed Alireza Hosseini",
"Gene Cheung",
"Philip Chou"
] | NeurIPS.cc/2024/Conference | 2406.04090 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=i8JaxY7tDI | @inproceedings{
cai2024textitreadme,
title={\${\textbackslash}textit\{Read-{ME}\}\$: Refactorizing {LLM}s as Router-Decoupled Mixture of Experts with System Co-Design},
author={Ruisi Cai and Yeonju Ro and Geon-Woo Kim and Peihao Wang and Babak Ehteshami Bejnordi and Aditya Akella and Zhangyang Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i8JaxY7tDI}
} | The proliferation of large language models (LLMs) has led to the adoption of Mixture-of-Experts (MoE) architectures that dynamically leverage specialized subnetworks for improved efficiency and performance. Despite their benefits, MoE models face significant challenges during inference, including inefficient memory management and suboptimal batching, due to misaligned design choices between the model architecture and the system policies. Furthermore, the conventional approach of training MoEs from scratch is increasingly prohibitive in terms of cost.
In this paper, we propose a novel framework $\textit{Read-ME}$ that transforms pre-trained dense LLMs into smaller MoE models (in contrast to ``upcycling" generalist MoEs), avoiding the high costs of ground-up training. Our approach employs activation sparsity to extract experts.
To compose experts, we examine the widely-adopted layer-wise router design and show its redundancy, and thus we introduce the pre-gating router decoupled from the MoE backbone that facilitates system-friendly pre-computing and lookahead scheduling, enhancing expert-aware batching and caching.
Our codesign therefore addresses critical gaps on both the algorithmic and system fronts, establishing a scalable and efficient alternative for LLM inference in resource-constrained settings.
$\textit{Read-ME}$ outperforms other popular open-source dense models of similar scales, achieving improvements of up to 10.1\% on MMLU, and improving mean end-to-end latency up to 6.1\%.
Codes are available at: \url{https://github.com/VITA-Group/READ-ME}. | Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design | [
"Ruisi Cai",
"Yeonju Ro",
"Geon-Woo Kim",
"Peihao Wang",
"Babak Ehteshami Bejnordi",
"Aditya Akella",
"Zhangyang Wang"
] | NeurIPS.cc/2024/Conference | 2410.19123 | [
""
] | https://huggingface.co/papers/2410.19123 | 2 | 15 | 2 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=i816TeqgVh | @inproceedings{
wang2024skild,
title={Ski{LD}: Unsupervised Skill Discovery Guided by Factor Interactions},
author={Zizhao Wang and Jiaheng Hu and Caleb Chuck and Stephen Chen and Roberto Mart{\'\i}n-Mart{\'\i}n and Amy Zhang and Scott Niekum and Peter Stone},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i816TeqgVh}
} | Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free interactions with environments. Existing unsupervised skill discovery methods learn skills by encouraging distinguishable behaviors that cover diverse states. However, in complex environments with many state factors (e.g., household environments with many objects), learning skills that cover all possible states is impossible, and naively encouraging state diversity often leads to simple skills that are not ideal for solving downstream tasks. This work introduces Skill Discovery from Local Dependencies (SkiLD), which leverages state factorization as a natural inductive bias to guide the skill learning process. The key intuition guiding SkiLD is that skills that induce \textbf{diverse interactions} between state factors are often more valuable for solving downstream tasks. To this end, SkiLD develops a novel skill learning objective that explicitly encourages the mastering of skills that effectively induce different interactions within an environment. We evaluate SkiLD in several domains with challenging, long-horizon sparse reward tasks including a realistic simulated household robot domain, where SkiLD successfully learns skills with clear semantic meaning and shows superior performance compared to existing unsupervised reinforcement learning methods that only maximize state coverage. | SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions | [
"Zizhao Wang",
"Jiaheng Hu",
"Caleb Chuck",
"Stephen Chen",
"Roberto Martín-Martín",
"Amy Zhang",
"Scott Niekum",
"Peter Stone"
] | NeurIPS.cc/2024/Conference | 2410.18416 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=i6BBclCymR | @inproceedings{
wang2024how,
title={How to Use Diffusion Priors under Sparse Views?},
author={Qisen Wang and Yifan Zhao and Jiawei Ma and Jia Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i6BBclCymR}
} | Novel view synthesis under sparse views has been a long-term important challenge in 3D reconstruction. Existing works mainly rely on introducing external semantic or depth priors to supervise the optimization of 3D representations. However, the diffusion model, as an external prior that can directly provide visual supervision, has always underperformed in sparse-view 3D reconstruction using Score Distillation Sampling (SDS) due to the low information entropy of sparse views compared to text, leading to optimization challenges caused by mode deviation. To this end, we present a thorough analysis of SDS from the mode-seeking perspective and propose Inline Prior Guided Score Matching (IPSM), which leverages visual inline priors provided by pose relationships between viewpoints to rectify the rendered image distribution and decomposes the original optimization objective of SDS, thereby offering effective diffusion visual guidance without any fine-tuning or pre-training. Furthermore, we propose the IPSM-Gaussian pipeline, which adopts 3D Gaussian Splatting as the backbone and supplements depth and geometry consistency regularization based on IPSM to further improve inline priors and rectified distribution. Experimental results on different public datasets show that our method achieves state-of-the-art reconstruction quality. The code is released at https://github.com/iCVTEAM/IPSM. | How to Use Diffusion Priors under Sparse Views? | [
"Qisen Wang",
"Yifan Zhao",
"Jiawei Ma",
"Jia Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=i5PoejmWoC | @inproceedings{
shah2024causal,
title={Causal language modeling can elicit search and reasoning capabilities on logic puzzles},
author={Kulin Shah and Nishanth Dikkala and Xin Wang and Rina Panigrahy},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i5PoejmWoC}
} | Causal language modeling using the Transformer architecture has yielded remarkable capabilities in Large Language Models (LLMs) over the last few years. However, the extent to which fundamental search and reasoning capabilities emerged within LLMs remains a topic of ongoing debate. In this work, we study if causal language modeling can learn a complex task such as solving Sudoku puzzles. To solve a Sudoku, the model is first required to search over all empty cells of the puzzle to decide on a cell to fill and then apply an appropriate strategy to fill the decided cell. Sometimes, the application of a strategy only results in thinning down the possible values in a cell rather than concluding the exact value of the cell. In such cases, multiple strategies are applied one after the other to fill a single cell. We observe that Transformer models trained on this synthetic task can indeed learn to solve Sudokus (our model solves $94.21\%$ of the puzzles fully correctly) when trained on a logical sequence of steps taken by a solver. We find that training Transformers with the logical sequence of steps is necessary and without such training, they fail to learn Sudoku. We also extend our analysis to Zebra puzzles (known as Einstein puzzles) and show that the model solves $92.04 \%$ of the puzzles fully correctly. In addition, we study the internal representations of the trained Transformer and find that through linear probing, we can decode information about the set of possible values in any given cell from them, pointing to the presence of a strong reasoning engine implicit in the Transformer weights. | Causal language modeling can elicit search and reasoning capabilities on logic puzzles | [
"Kulin Shah",
"Nishanth Dikkala",
"Xin Wang",
"Rina Panigrahy"
] | NeurIPS.cc/2024/Conference | 2409.10502 | [
"https://github.com/kulinshah98/llm-reasoning-logic-puzzles"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=i4jZ6fCDdy | @inproceedings{
delden2024learning,
title={Learning to Predict Structural Vibrations},
author={Jan van Delden and Julius Schultz and Christopher Blech and Sabine C. Langer and Timo L{\"u}ddecke},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i4jZ6fCDdy}
} | In mechanical structures like airplanes, cars and houses, noise is generated and transmitted through vibrations. To take measures to reduce this noise, vibrations need to be simulated with expensive numerical computations. Deep learning surrogate models present a promising alternative to classical numerical simulations as they can be evaluated magnitudes faster, while trading-off accuracy. To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates. The benchmark features a total of 12,000 plate geometries with varying forms of beadings, material, boundary conditions, load position and sizes with associated numerical solutions.
To address the benchmark task, we propose a new network architecture, named \modelname, which predicts vibration patterns of plate geometries given a specific excitation frequency. Applying principles from operator learning and implicit models for shape encoding, our approach effectively addresses the prediction of highly variable frequency response functions occurring in dynamic systems. To quantify the prediction quality, we introduce a set of evaluation metrics and evaluate the method on our vibrating-plates benchmark. Our method outperforms DeepONets, Fourier Neural Operators and more traditional neural network architectures and can be used for design optimization.
Code, dataset and visualizations: https://github.com/ecker-lab/Learning_Vibrating_Plates | Learning to Predict Structural Vibrations | [
"Jan van Delden",
"Julius Schultz",
"Christopher Blech",
"Sabine C. Langer",
"Timo Lüddecke"
] | NeurIPS.cc/2024/Conference | 2310.05469 | [
"https://github.com/ecker-lab/FQ-Operator"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=i4gqCM1r3z | @inproceedings{
bertran2024reconstruction,
title={Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable},
author={Martin Andres Bertran and Shuai Tang and Michael Kearns and Jamie Heather Morgenstern and Aaron Roth and Steven Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i4gqCM1r3z}
} | Machine unlearning is motivated by principles of data autonomy. The premise is that a person can request to have their data's influence removed from deployed models, and those models should be updated as if they were retrained without the person's data. We show that these updates expose individuals to high-accuracy reconstruction attacks which allow the attacker to recover their data in its entirety, even when the original models are so simple that privacy risk might not otherwise have been a concern. We show how to mount a near-perfect attack on the deleted data point from linear regression models. We then generalize our attack to other loss functions and architectures, and empirically demonstrate the effectiveness of our attacks across a wide range of datasets (capturing both tabular and image data). Our work highlights that privacy risk is significant even for extremely simple model classes when individuals can request deletion of their data from the model. | Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable | [
"Martin Andres Bertran",
"Shuai Tang",
"Michael Kearns",
"Jamie Heather Morgenstern",
"Aaron Roth",
"Steven Wu"
] | NeurIPS.cc/2024/Conference | 2405.20272 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=i4MutM2TZb | @inproceedings{
zhou2024pretrained,
title={Pre-trained Large Language Models Use Fourier Features to Compute Addition},
author={Tianyi Zhou and Deqing Fu and Vatsal Sharan and Robin Jia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i4MutM2TZb}
} | Pre-trained large language models (LLMs) exhibit impressive mathematical reasoning capabilities, yet how they compute basic arithmetic, such as addition, remains unclear.
This paper shows that pre-trained LLMs add numbers using Fourier features---dimensions in the hidden state that represent numbers via a set of features sparse in the frequency domain.
Within the model, MLP and attention layers use Fourier features in complementary ways: MLP layers primarily approximate the magnitude of the answer using low-frequency features, while attention layers primarily perform modular addition (e.g., computing whether the answer is even or odd) using high-frequency features.
Pre-training is crucial for this mechanism: models trained from scratch to add numbers only exploit low-frequency features, leading to lower accuracy.
Introducing pre-trained token embeddings to a randomly initialized model rescues its performance.
Overall, our analysis demonstrates that appropriate pre-trained representations (e.g., Fourier features) can unlock the ability of Transformers to learn precise mechanisms for algorithmic tasks. | Pre-trained Large Language Models Use Fourier Features to Compute Addition | [
"Tianyi Zhou",
"Deqing Fu",
"Vatsal Sharan",
"Robin Jia"
] | NeurIPS.cc/2024/Conference | 2406.03445 | [
""
] | https://huggingface.co/papers/2406.03445 | 1 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=i3me9bCSCy | @inproceedings{
andreis2024setbased,
title={Set-based Neural Network Encoding Without Weight Tying},
author={Bruno Andreis and Bedionita Soro and Philip Torr and Sung Ju Hwang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i3me9bCSCy}
} | We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions
to efficiently encode neural network parameters. Our approach is capable of encoding neural networks in a model zoo of mixed architecture and different parameter sizes as opposed to previous approaches that require custom encoding models for different architectures. Furthermore, our \textbf{S}et-based \textbf{N}eural network \textbf{E}ncoder (SNE) takes into consideration the hierarchical computational structure of neural networks. To respect symmetries inherent in network weight space, we utilize Logit Invariance to learn the required minimal invariance properties. Additionally, we introduce a \textit{pad-chunk-encode} pipeline to efficiently encode neural network layers that is adjustable to computational and memory constraints. We also introduce two new tasks for neural network property prediction: cross-dataset and cross-architecture. In cross-dataset property prediction, we evaluate how well property predictors generalize across model zoos trained on different datasets but of the same architecture. In cross-architecture property prediction, we evaluate how well property predictors transfer to model zoos of different architecture not seen during training. We show that SNE outperforms the relevant baselines on standard benchmarks. | Set-based Neural Network Encoding Without Weight Tying | [
"Bruno Andreis",
"Bedionita Soro",
"Philip Torr",
"Sung Ju Hwang"
] | NeurIPS.cc/2024/Conference | 2305.16625 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=i2oacRDF5L | @inproceedings{
bramblett2024beliefstate,
title={Belief-State Query Policies for User-Aligned Planning under Partial Observability},
author={Daniel Richard Bramblett and Siddharth Srivastava},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i2oacRDF5L}
} | Planning in real-world settings often entails addressing partial observability while aligning with users' requirements. We present a novel framework for expressing users' constraints and preferences about agent behavior in a partially observable setting using parameterized belief-state query (BSQ) constraints in the setting of goal-oriented partially observable Markov decision processes (gPOMDPs). We present the first formal analysis of such constraints and prove that while the expected cost of a BSQ constraint is not a convex function w.r.t its parameters, it is piecewise constant and yields an implicit discrete parameter search space that is finite for finite horizons. This theoretical result leads to novel algorithms that optimize gPOMDP agent behavior with guaranteed user alignment. Theoretical analysis proves that our algorithms converge to the optimal user-aligned behavior in the limit. Empirical results show that BSQ constraints provide a computationally feasible approach for user-aligned planning in partially observable settings. | Belief-State Query Policies for User-Aligned Planning under Partial Observability | [
"Daniel Richard Bramblett",
"Siddharth Srivastava"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=i1xjK5a0X8 | @inproceedings{
zhang2024pcpmae,
title={{PCP}-{MAE}: Learning to Predict Centers for Point Masked Autoencoders},
author={Xiangdong Zhang and Shaofeng Zhang and Junchi Yan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=i1xjK5a0X8}
} | Masked autoencoder has been widely explored in point cloud self-supervised learning, whereby the point cloud is generally divided into visible and masked parts. These methods typically include an encoder accepting visible patches (normalized) and corresponding patch centers (position) as input, with the decoder accepting the output of the encoder and the centers (position) of the masked parts to reconstruct each point in the masked patches. Then, the pre-trained encoders are used for downstream tasks. In this paper, we show a motivating empirical result that when directly feeding the centers of masked patches to the decoder without information from the encoder, it still reconstructs well. In other words, the centers of patches are important and the reconstruction objective does not necessarily rely on representations of the encoder, thus preventing the encoder from learning semantic representations. Based on this key observation, we propose a simple yet effective method, $i.e.$, learning to \textbf{P}redict \textbf{C}enters for \textbf{P}oint \textbf{M}asked \textbf{A}uto\textbf{E}ncoders (\textbf{PCP-MAE}) which guides the model to learn to predict the significant centers and use the predicted centers to replace the directly provided centers. Specifically, we propose a Predicting Center Module (PCM) that shares parameters with the original encoder with extra cross-attention to predict centers. Our method is of high pre-training efficiency compared to other alternatives and achieves great improvement over Point-MAE, particularly surpassing it by \textbf{5.50\% on OBJ-BG, 6.03\% on OBJ-ONLY, and 5.17\% on PB-T50-RS} for 3D object classification on the ScanObjectNN dataset. The code is available at \url{https://github.com/aHapBean/PCP-MAE}. | PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders | [
"Xiangdong Zhang",
"Shaofeng Zhang",
"Junchi Yan"
] | NeurIPS.cc/2024/Conference | 2408.08753 | [
"https://github.com/aHapBean/PCP-MAE"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=hwuUBsMlBf | @inproceedings{
li2024cumo,
title={CuMo: Scaling Multimodal {LLM} with Co-Upcycled Mixture-of-Experts},
author={Jiachen Li and Xinyao Wang and Sijie Zhu and Chia-Wen Kuo and Lu XU and Fan Chen and Jitesh Jain and Humphrey Shi and Longyin Wen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hwuUBsMlBf}
} | Recent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on multimodal tasks. However, these scaling approaches are computationally expensive and overlook the significance of efficiently improving model capabilities from the vision side.
Inspired by the successful applications of Mixture-of-Experts (MoE) in LLMs, which improves model scalability during training while keeping inference costs similar to those of smaller models, we propose CuMo, which incorporates Co-upcycled Top-K sparsely-gated Mixture-of-experts blocks into both the vision encoder and the MLP connector, thereby enhancing the multimodal LLMs with neglectable additional activated parameters during inference.
CuMo first pre-trains the MLP blocks and then initializes each expert in the MoE block from the pre-trained MLP block during the visual instruction tuning stage, with auxiliary losses to ensure a balanced loading of experts.
CuMo outperforms state-of-the-art multimodal LLMs across various VQA and visual-instruction-following benchmarks within each model size group, all while training exclusively on open-sourced datasets. | CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts | [
"Jiachen Li",
"Xinyao Wang",
"Sijie Zhu",
"Chia-Wen Kuo",
"Lu XU",
"Fan Chen",
"Jitesh Jain",
"Humphrey Shi",
"Longyin Wen"
] | NeurIPS.cc/2024/Conference | 2405.05949 | [
"https://github.com/shi-labs/cumo"
] | https://huggingface.co/papers/2405.05949 | 0 | 2 | 0 | 9 | [] | [] | [
"shi-labs/CuMo-7b-zero"
] | [] | [] | [
"shi-labs/CuMo-7b-zero"
] | 1 | poster |
null | https://openreview.net/forum?id=hw76X5uWrc | @inproceedings{
meyerson2024unlocking,
title={Unlocking the Potential of Global Human Expertise},
author={Elliot Meyerson and Olivier Francon and Darren Sargent and Babak Hodjat and Risto Miikkulainen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hw76X5uWrc}
} | Solving societal problems on a global scale requires the collection and processing of ideas and methods from diverse sets of international experts. As the number and diversity of human experts increase, so does the likelihood that elements in this collective knowledge can be combined and refined to discover novel and better solutions. However, it is difficult to identify, combine, and refine complementary information in an increasingly large and diverse knowledge base. This paper argues that artificial intelligence (AI) can play a crucial role in this process. An evolutionary AI framework, termed RHEA, fills this role by distilling knowledge from diverse models created by human experts into equivalent neural networks, which are then recombined and refined in a population-based search. The framework was implemented in a formal synthetic domain, demonstrating that it is transparent and systematic. It was then applied to the results of the XPRIZE Pandemic Response Challenge, in which over 100 teams of experts across 23 countries submitted models based on diverse methodologies to predict COVID-19 cases and suggest non-pharmaceutical intervention policies for 235 nations, states, and regions across the globe. Building upon this expert knowledge, by recombining and refining the 169 resulting policy suggestion models, RHEA discovered a broader and more effective set of policies than either AI or human experts alone, as evaluated based on real-world data. The results thus suggest that AI can play a crucial role in realizing the potential of human expertise in global problem-solving. | Unlocking the Potential of Global Human Expertise | [
"Elliot Meyerson",
"Olivier Francon",
"Darren Sargent",
"Babak Hodjat",
"Risto Miikkulainen"
] | NeurIPS.cc/2024/Conference | 2411.00156 | [
"https://github.com/cognizant-ai-labs/rhea-demo"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hsgNvC5YM9 | @inproceedings{
park2024constant,
title={Constant Acceleration Flow},
author={Dogyun Park and Sojin Lee and Sihyeon Kim and Taehoon Lee and Youngjoon Hong and Hyunwoo J. Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hsgNvC5YM9}
} | Rectified flow and reflow procedures have significantly advanced fast generation by progressively straightening ordinary differential equation (ODE) flows under the assumption that image and noise pairs, known as coupling, can be approximated by straight trajectories with constant velocity. However, we observe that the constant velocity modeling and reflow procedures have limitations in accurately learning to couple with flow crossing, leading to suboptimal few-step generation. To overcome the limitations, we introduce the Constant Acceleration Flow (CAF), a novel framework based on a simple constant acceleration equation. Additionally, we propose two techniques to improve estimation accuracy: initial velocity conditioning for the acceleration model and a reflow process for the initial velocity. Our comparative studies show that CAF not only outperforms rectified flow with reflow procedures in terms of speed and accuracy but also demonstrates substantial improvements in preserving coupling for fast generation. | Constant Acceleration Flow | [
"Dogyun Park",
"Sojin Lee",
"Sihyeon Kim",
"Taehoon Lee",
"Youngjoon Hong",
"Hyunwoo J. Kim"
] | NeurIPS.cc/2024/Conference | 2411.00322 | [
""
] | https://huggingface.co/papers/2411.00322 | 3 | 22 | 3 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=hpvJwmzEHX | @inproceedings{
koziarski2024rgfn,
title={{RGFN}: Synthesizable Molecular Generation Using {GF}lowNets},
author={Micha{\l} Koziarski and Andrei Rekesh and Dmytro Shevchuk and Almer M. van der Sloot and Piotr Gai{\'n}ski and Yoshua Bengio and Cheng-Hao Liu and Mike Tyers and Robert A. Batey},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hpvJwmzEHX}
} | Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking. | RGFN: Synthesizable Molecular Generation Using GFlowNets | [
"Michał Koziarski",
"Andrei Rekesh",
"Dmytro Shevchuk",
"Almer M. van der Sloot",
"Piotr Gaiński",
"Yoshua Bengio",
"Cheng-Hao Liu",
"Mike Tyers",
"Robert A. Batey"
] | NeurIPS.cc/2024/Conference | 2406.08506 | [
"https://github.com/koziarskilab/rgfn"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hocAc3Qit7 | @inproceedings{
iyer2024flexible,
title={Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals},
author={Abhiram Iyer and Sarthak Chandra and Sugandha Sharma and Ila R Fiete},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hocAc3Qit7}
} | Grid cells in the medial entorhinal cortex create remarkable periodic maps of explored space during navigation. Recent studies show that they form similar maps of abstract cognitive spaces. Examples of such abstract environments include auditory tone sequences in which the pitch is continuously varied or images in which abstract features are continuously deformed (e.g., a cartoon bird whose legs stretch and shrink). Here, we hypothesize that the brain generalizes how it maps spatial domains to mapping abstract spaces.
To sidestep the computational cost of learning representations for each high-dimensional sensory input, the brain extracts self-consistent, low-dimensional descriptions of displacements across abstract spaces, leveraging the spatial velocity integration of grid cells to efficiently build maps of different domains.
Our neural network model for abstract velocity extraction factorizes the content of these abstract domains from displacements within the domains to generate content-independent and self-consistent, low-dimensional velocity estimates.
Crucially, it uses a self-supervised geometric consistency constraint that requires displacements along closed loop trajectories to sum to zero, an integration that is itself performed by the downstream grid cell circuit over learning. This process results in high fidelity estimates of velocities and allowed transitions in abstract domains, a crucial prerequisite for efficient map generation in these high-dimensional environments. We also show how our method outperforms traditional dimensionality reduction and deep-learning based motion extraction networks on the same set of tasks.
This is the first neural network model to explain how grid cells can flexibly represent different abstract spaces and makes the novel prediction that they should do so while maintaining their population correlation and manifold structure across domains. Fundamentally, our model sheds light on the mechanistic origins of cognitive flexibility and transfer of representations across vastly different domains in brains, providing a potential self-supervised learning (SSL) framework for leveraging similar ideas in transfer learning and data-efficient generalization in machine learning and robotics. | Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals | [
"Abhiram Iyer",
"Sarthak Chandra",
"Sugandha Sharma",
"Ila R Fiete"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hoVXLC8vQU | @inproceedings{
anagnostides2024convergence,
title={Convergence of \${\textbackslash}text\{log\}(1/{\textbackslash}epsilon)\$ for Gradient-Based Algorithms in Zero-Sum Games without the Condition Number: A Smoothed Analysis},
author={Ioannis Anagnostides and Tuomas Sandholm},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hoVXLC8vQU}
} | Gradient-based algorithms have shown great promise in solving large (two-player) zero-sum games. However, their success has been mostly confined to the low-precision regime since the number of iterations grows polynomially in $1/\epsilon$, where $\epsilon > 0$ is the duality gap. While it has been well-documented that linear convergence---an iteration complexity scaling as $\text{log}(1/\epsilon)$---can be attained even with gradient-based algorithms, that comes at the cost of introducing a dependency on certain condition number-like quantities which can be exponentially large in the description of the game. To address this shortcoming, we examine the iteration complexity of several gradient-based algorithms in the celebrated framework of smoothed analysis, and we show that they have polynomial smoothed complexity, in that their number of iterations grows as a polynomial in the dimensions of the game, $\text{log}(1/\epsilon)$, and $1/\sigma$, where $\sigma$ measures the magnitude of the smoothing perturbation. Our result applies to optimistic gradient and extra-gradient descent/ascent, as well as a certain iterative variant of Nesterov's smoothing technique. From a technical standpoint, the proof proceeds by characterizing and performing a smoothed analysis of a certain error bound, the key ingredient driving linear convergence in zero-sum games. En route, our characterization also makes a natural connection between the convergence rate of such algorithms and perturbation-stability properties of the equilibrium, which is of interest beyond the model of smoothed complexity. | Convergence of log(1/ϵ) for Gradient-Based Algorithms in Zero-Sum Games without the Condition Number: A Smoothed Analysis | [
"Ioannis Anagnostides",
"Tuomas Sandholm"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hkujvAPVsg | @inproceedings{
gutierrez2024hipporag,
title={Hippo{RAG}: Neurobiologically Inspired Long-Term Memory for Large Language Models},
author={Bernal Jimenez Gutierrez and Yiheng Shu and Yu Gu and Michihiro Yasunaga and Yu Su},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hkujvAPVsg}
} | In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting. Despite the impressive accomplishments, large language models (LLMs), even with retrieval-augmented generation (RAG), still struggle to efficiently and effectively integrate a large amount of new experiences after pre-training. In this work, we introduce HippoRAG, a novel retrieval framework inspired by the hippocampal indexing theory of human long-term memory to enable deeper and more efficient knowledge integration over new experiences. HippoRAG synergistically orchestrates LLMs, knowledge graphs, and the Personalized PageRank algorithm to mimic the different roles of neocortex and hippocampus in human memory. We compare HippoRAG with existing RAG methods on multi-hop question answering (QA) and show that our method outperforms the state-of-the-art methods remarkably, by up to 20%. Single-step retrieval with HippoRAG achieves comparable or better performance than iterative retrieval like IRCoT while being 10-20 times cheaper and 6-13 times faster, and integrating HippoRAG into IRCoT brings further substantial gains. Finally, we show that our method can tackle new types of scenarios that are out of reach of existing methods. | HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models | [
"Bernal Jimenez Gutierrez",
"Yiheng Shu",
"Yu Gu",
"Michihiro Yasunaga",
"Yu Su"
] | NeurIPS.cc/2024/Conference | 2405.14831 | [
"https://github.com/osu-nlp-group/hipporag"
] | https://huggingface.co/papers/2405.14831 | 3 | 3 | 2 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=hkEwwAqmCk | @inproceedings{
li2024daada,
title={{DA}-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection},
author={Haochen Li and Rui Zhang and Hantao Yao and Xin Zhang and Yifan Hao and Xinkai Song and Xiaqing Li and Yongwei Zhao and Yunji Chen and Ling Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hkEwwAqmCk}
} | Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain.
As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domain-invariant knowledge for DAOD.
However, the domain-agnostic adapter is inevitably biased to the source domain.
It discards some beneficial knowledge discriminative on the unlabelled domain, \ie domain-specific knowledge of the target domain.
To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task.
The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge.
DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder.
Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection.
Our code is available at https://github.com/Therock90421/DA-Ada. | DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection | [
"Haochen Li",
"Rui Zhang",
"Hantao Yao",
"Xin Zhang",
"Yifan Hao",
"Xinkai Song",
"Xiaqing Li",
"Yongwei Zhao",
"Yunji Chen",
"Ling Li"
] | NeurIPS.cc/2024/Conference | 2410.09004 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hkBhX5ABjk | @inproceedings{
jiang2024multiagent,
title={Multi-Agent Domain Calibration with a Handful of Offline Data},
author={Tao Jiang and Lei Yuan and Lihe Li and Cong Guan and Zongzhang Zhang and Yang Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hkBhX5ABjk}
} | The shift in dynamics results in significant performance degradation of policies trained in the source domain when deployed in a different target domain, posing a challenge for the practical application of reinforcement learning (RL) in real-world scenarios. Domain transfer methods aim to bridge this dynamics gap through techniques such as domain adaptation or domain calibration. While domain adaptation involves refining the policy through extensive interactions in the target domain, it may not be feasible for sensitive fields like healthcare and autonomous driving. On the other hand, offline domain calibration utilizes only static data from the target domain to adjust the physics parameters of the source domain (e.g., a simulator) to align with the target dynamics, enabling the direct deployment of the trained policy without sacrificing performance, which emerges as the most promising for policy deployment. However, existing techniques primarily rely on evolution algorithms for calibration, resulting in low sample efficiency.
To tackle this issue, we propose a novel framework Madoc (\textbf{M}ulti-\textbf{a}gent \textbf{do}main \textbf{c}alibration). Firstly, we formulate a bandit RL objective to match the target trajectory distribution by learning a couple of classifiers. We then address the challenge of a large domain parameter space by modeling domain calibration as a cooperative multi-agent reinforcement learning (MARL) problem. Specifically, we utilize a Variational Autoencoder (VAE) to automatically cluster physics parameters with similar effects on the dynamics, grouping them into distinct agents. These grouped agents train calibration policies coordinately to adjust multiple parameters using MARL.
Our empirical evaluation on 21 offline locomotion tasks in D4RL and NeoRL benchmarks showcases the superior performance of our method compared to strong existing offline model-based RL, offline domain calibration, and hybrid offline-and-online RL baselines. | Multi-Agent Domain Calibration with a Handful of Offline Data | [
"Tao Jiang",
"Lei Yuan",
"Lihe Li",
"Cong Guan",
"Zongzhang Zhang",
"Yang Yu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hjspWd7jvg | @inproceedings{
klein2024genot,
title={{GENOT}: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics},
author={Dominik Klein and Th{\'e}o Uscidda and Fabian J Theis and marco cuturi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hjspWd7jvg}
} | Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However,
single-cell sequencing technologies are inherently destructive and can only measure a limited array of data modalities simultaneously. This limitation underscores
the need for new methods capable of realigning cells. Optimal transport (OT)
has emerged as a potent solution, but traditional discrete solvers are hampered by
scalability, privacy, and out-of-sample estimation issues. These challenges have
spurred the development of neural network-based solvers, known as neural OT
solvers, that parameterize OT maps. Yet, these models often lack the flexibility
needed for broader life science applications. To address these deficiencies, our
approach learns stochastic maps (i.e. transport plans), allows for any cost function,
relaxes mass conservation constraints and integrates quadratic solvers to tackle the
complex challenges posed by the (Fused) Gromov-Wasserstein problem. Utilizing
flow matching as a backbone, our method offers a flexible and effective framework.
We demonstrate its versatility and robustness through applications in cell development studies, cellular drug response modeling, and cross-modality cell translation,
illustrating significant potential for enhancing therapeutic strategies. | GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics | [
"Dominik Klein",
"Théo Uscidda",
"Fabian J Theis",
"marco cuturi"
] | NeurIPS.cc/2024/Conference | 2310.09254 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hjhpCJfbFG | @inproceedings{
ma2024interpretable,
title={Interpretable Image Classification with Adaptive Prototype-based Vision Transformers},
author={Chiyu Ma and Jon Donnelly and Wenjun Liu and Soroush Vosoughi and Cynthia Rudin and Chaofan Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hjhpCJfbFG}
} | We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning. This method classifies an image by comparing it to a set of learned prototypes, providing explanations of the form ``this looks like that.'' In our model, a prototype consists of **parts**, which can deform over irregular geometries to create a better comparison between images. Unlike existing models that rely on Convolutional Neural Network (CNN) backbones and spatially rigid prototypes, our model integrates Vision Transformer (ViT) backbones into prototype based models, while offering spatially deformed prototypes that not only accommodate geometric variations of objects but also provide coherent and clear prototypical feature representations with an adaptive number of prototypical parts. Our experiments show that our model can generally achieve higher performance than the existing prototype based models. Our comprehensive analyses ensure that the prototypes are consistent and the interpretations are faithful. | Interpretable Image Classification with Adaptive Prototype-based Vision Transformers | [
"Chiyu Ma",
"Jon Donnelly",
"Wenjun Liu",
"Soroush Vosoughi",
"Cynthia Rudin",
"Chaofan Chen"
] | NeurIPS.cc/2024/Conference | 2410.20722 | [
"https://github.com/Henrymachiyu/ProtoViT"
] | https://huggingface.co/papers/2410.20722 | 1 | 1 | 0 | 6 | [
"chiyum609/ProtoViT"
] | [] | [] | [
"chiyum609/ProtoViT"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=hilGwNabqB | @inproceedings{
makhija2024a,
title={A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings},
author={Disha Makhija and Joydeep Ghosh and Nhat Ho},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hilGwNabqB}
} | Federated learning (FL), through its privacy-preserving collaborative learning approach, has significantly empowered decentralized devices. However, constraints in either data and/or computational resources among participating clients introduce several challenges in learning, including the inability to train large model architectures, heightened risks of overfitting, and more. In this work, we present a novel FL framework grounded in Bayesian learning to address these challenges. Our approach involves training personalized Bayesian models at each client tailored to the unique complexities of the clients' datasets and efficiently collaborating across these clients. By leveraging Bayesian neural networks and their uncertainty quantification capabilities, our local training procedure robustly learns from small datasets. And the novel collaboration procedure utilizing priors in the functional (output) space of the networks facilitates collaboration across models of varying sizes, enabling the framework to adapt well in heterogeneous data and computational settings. Furthermore, we present a differentially private version of the algorithm, accompanied by formal differential privacy guarantees that apply without any assumptions on the learning algorithm. Through experiments on popular FL datasets, we demonstrate that our approach outperforms strong baselines in both homogeneous and heterogeneous settings, and under strict privacy constraints. | A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings | [
"Disha Makhija",
"Joydeep Ghosh",
"Nhat Ho"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hhnkH8ex5d | @inproceedings{
srivastava2024precipitation,
title={Precipitation Downscaling with Spatiotemporal Video Diffusion},
author={Prakhar Srivastava and Ruihan Yang and Gavin Kerrigan and Gideon Dresdner and Jeremy J McGibbon and Christopher S. Bretherton and Stephan Mandt},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hhnkH8ex5d}
} | In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a low-resolution prediction is improved using statistical approaches. Unlike traditional computer vision tasks, weather and climate applications require capturing the accurate conditional distribution of high-resolution given low-resolution patterns to assure reliable ensemble averages and unbiased estimates of extreme events, such as heavy rain. This work extends recent video diffusion models to precipitation super-resolution, employing a deterministic downscaler followed by a temporally-conditioned diffusion model to capture noise characteristics and high-frequency patterns. We test our approach on FV3GFS output, an established large-scale global atmosphere model, and compare it against six state-of-the-art baselines. Our analysis, capturing CRPS, MSE, precipitation distributions, and qualitative aspects using California and the Himalayas as examples, establishes our method as a new standard for data-driven precipitation downscaling. | Precipitation Downscaling with Spatiotemporal Video Diffusion | [
"Prakhar Srivastava",
"Ruihan Yang",
"Gavin Kerrigan",
"Gideon Dresdner",
"Jeremy J McGibbon",
"Christopher S. Bretherton",
"Stephan Mandt"
] | NeurIPS.cc/2024/Conference | 2312.06071 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hgsS4onO4s | @inproceedings{
kim2024inverse,
title={Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions},
author={Hideaki Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hgsS4onO4s}
} | Kernel methods are widely utilized in machine learning field to learn, from training data, a latent function in a reproducing kernel Hilbert space. It is well known that the approximator thus obtained usually achieves a linear representation, which brings various computational benefits, while maintaining great representation power (i.e., universal approximation). However, when non-negativity constraints are imposed on the function's outputs, the literature usually takes the kernel method-based approximators as offering linear representations at the expense of limited model flexibility or good representation power by allowing for their nonlinear forms. The main contribution of this paper is to derive a sufficient condition for a positive definite kernel so that it may construct flexible and linear approximators of non-negative functions. We call a kernel function that offers these attributes an *inverse M-kernel*; it is reminiscent of the inverse M-matrix. Furthermore, we show that for a one-dimensional input space, universal exponential/Abel kernels are inverse M-kernels and construct linear universal approximators of non-negative functions. To the best of our knowledge, it is the first time that the existence of linear universal approximators of non-negative functions has been elucidated. We confirm the effectiveness of our results by experiments on the problems of non-negativity-constrained regression, density estimation, and intensity estimation. Finally, we discuss issues and perspectives on multi-dimensional input settings. | Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions | [
"Hideaki Kim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hgdh4foghu | @inproceedings{
hutson2024policyshaped,
title={Policy-shaped prediction: avoiding distractions in model-based reinforcement learning},
author={Miles Richard Hutson and Isaac Kauvar and Nick Haber},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hgdh4foghu}
} | Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods ---including DreamerV3 and DreamerPro--- with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through a synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning. | Policy-shaped prediction: avoiding distractions in model-based reinforcement learning | [
"Miles Richard Hutson",
"Isaac Kauvar",
"Nick Haber"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hdUCZiMkFO | @inproceedings{
ma2024happy,
title={Happy: A Debiased Learning Framework for Continual Generalized Category Discovery},
author={Shijie Ma and Fei Zhu and Zhun Zhong and Wenzhuo Liu and Xu-Yao Zhang and Cheng-Lin Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hdUCZiMkFO}
} | Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from *unlabeled* data while maintaining the ability to recognize previously learned classes. Although several settings are proposed to study the C-GCD task, they have limitations that do not reflect real-world scenarios. We thus study a more practical C-GCD setting, which includes more new classes to be discovered over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data of known classes, followed by multiple incremental stages where the model is fed with unlabeled data containing both old and new classes. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We delve into the conflicts and identify that models are susceptible to *prediction bias* and *hardness bias*. To address these issues, we introduce a debiased learning framework, namely **Happy**, characterized by **H**ardness-**a**ware **p**rototype sampling and soft entro**py** regularization. For the *prediction bias*, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, which can significantly enhance the clustering performance of new classes. For the *harness bias*, we present the hardness-aware prototype sampling, which can effectively reduce the forgetting issue for previously seen classes, especially for difficult classes. Experimental results demonstrate our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various datasets, e.g., 7.5% overall gains on ImageNet-100. Our code is publicly available at https://github.com/mashijie1028/Happy-CGCD. | Happy: A Debiased Learning Framework for Continual Generalized Category Discovery | [
"Shijie Ma",
"Fei Zhu",
"Zhun Zhong",
"Wenzhuo Liu",
"Xu-Yao Zhang",
"Cheng-Lin Liu"
] | NeurIPS.cc/2024/Conference | 2410.06535 | [
"https://github.com/mashijie1028/happy-cgcd"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hbOWLtJNMK | @inproceedings{
li2024longrange,
title={Long-range Meta-path Search on Large-scale Heterogeneous Graphs},
author={Chao Li and Zijie Guo and Qiuting He and Kun He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hbOWLtJNMK}
} | Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational costs while maximizing effective information utilization in the presence of heterogeneity, and overcoming the over-smoothing issue in graph neural networks. To address this gap, we investigate the importance of different meta-paths and introduce
an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS). Specifically, we develop a search space with all meta-paths related to the target node type. By employing a progressive sampling algorithm, LMSPS dynamically shrinks the search space with hop-independent time complexity. Through a sampling evaluation strategy, LMSPS conducts a specialized and effective meta-path selection, leading to retraining with only effective meta-paths, thus mitigating costs and over-smoothing. Extensive experiments across diverse heterogeneous datasets validate LMSPS's capability in discovering effective long-range meta-paths, surpassing state-of-the-art methods. Our code is available at https://github.com/JHL-HUST/LMSPS. | Long-range Meta-path Search on Large-scale Heterogeneous Graphs | [
"Chao Li",
"Zijie Guo",
"Qiuting He",
"Kun He"
] | NeurIPS.cc/2024/Conference | 2307.08430 | [
"https://github.com/jhl-hust/ldmlp"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=haa457jwjw | @inproceedings{
zhang2024piecewisestationary,
title={Piecewise-Stationary Bandits with Knapsacks},
author={Xilin Zhang and Wang Chi Cheung},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=haa457jwjw}
} | We study Bandits with Knapsacks (Bwk) in a piecewise-stationary environment. We propose a novel inventory reserving algorithm which draws new insights into the problem. Suppose parameters $\eta_{\min}, \eta_{\max} \in (0,1]$ respectively lower and upper bound the reward earned and the resources consumed in a time round. Our algorithm achieves a provably near-optimal competitive ratio of $O(\log(\eta_{\max}/\eta_{\min}))$, with a matching lower bound provided. Our performance guarantee is based on a dynamic benchmark, distinguishing our work from existing works on adversarial Bwk who compare with the static benchmark. Furthermore, different from existing non-stationary Bwk work, we do not require a bounded global variation. | Piecewise-Stationary Bandits with Knapsacks | [
"Xilin Zhang",
"Wang Chi Cheung"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=haVPmN8UGi | @inproceedings{
deng2024graphvis,
title={GraphVis: Boosting {LLM}s with Visual Knowledge Graph Integration},
author={Yihe Deng and Chenchen Ye and Zijie Huang and Mingyu Derek Ma and Yiwen Kou and Wei Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=haVPmN8UGi}
} | The rapid evolution of large language models (LLMs) has expanded their capabilities across various data modalities, extending from well-established image data to increasingly popular graph data. Given the limitation of LLMs in hallucinations and inaccuracies in recalling factual knowledge, Knowledge Graph (KG) has emerged as a crucial data modality to support more accurate reasoning by LLMs. However, integrating structured knowledge from KGs into LLMs remains challenging, as most current KG-enhanced LLM methods directly convert the KG into linearized text triples, which is not as expressive as the original structured data. To address this, we introduce GraphVis, which conserves the intricate graph structure through the visual modality to enhance the comprehension of KGs with the aid of Large Vision Language Models (LVLMs). Our approach incorporates a unique curriculum fine-tuning scheme which first instructs LVLMs to recognize basic graphical features from the images, and subsequently incorporates reasoning on QA tasks with the visual graphs. This cross-modal methodology not only markedly enhances performance on standard textual QA but also shows improved zero-shot VQA performance by utilizing synthetic graph images to augment the data for VQA tasks. We present comprehensive evaluations across commonsense reasoning QA benchmarks, where GraphVis provides an average improvement of 11.1% over its base model and outperforms existing KG-enhanced LLM approaches. Across VQA benchmarks such as ScienceQA that share similar scientific diagram images, GraphVis provides a notable gain of 4.32%. | GraphVis: Boosting LLMs with Visual Knowledge Graph Integration | [
"Yihe Deng",
"Chenchen Ye",
"Zijie Huang",
"Mingyu Derek Ma",
"Yiwen Kou",
"Wei Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=haUnEiXgQ7 | @inproceedings{
wei2024visionlanguage,
title={Vision-Language Models are Strong Noisy Label Detectors},
author={Tong Wei and Hao-Tian Li and Chun-Shu Li and Jiang-Xin Shi and Yu-Feng Li and Min-Ling Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=haUnEiXgQ7}
} | Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the fine-tuning process. To address this challenge, this paper presents a Denoising Fine-Tuning framework, called DeFT, for adapting vision-language models. DeFT utilizes the robust alignment of textual and visual features pre-trained on millions of auxiliary image-text pairs to sieve out noisy labels. The proposed framework establishes a noisy label detector by learning positive and negative textual prompts for each class. The positive prompt seeks to reveal distinctive features of the class, while the negative prompt serves as a learnable threshold for separating clean and noisy samples. We employ parameter-efficient fine-tuning for the adaptation of a pre-trained visual encoder to promote its alignment with the learned textual prompts. As a general framework, DeFT can seamlessly fine-tune many pre-trained models to downstream tasks by utilizing carefully selected clean samples. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DeFT in both noisy label detection and image classification. Our source code can be found in the supplementary material. | Vision-Language Models are Strong Noisy Label Detectors | [
"Tong Wei",
"Hao-Tian Li",
"Chun-Shu Li",
"Jiang-Xin Shi",
"Yu-Feng Li",
"Min-Ling Zhang"
] | NeurIPS.cc/2024/Conference | 2409.19696 | [
"https://github.com/HotanLee/DeFT"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=haSKMlrbX5 | @inproceedings{
gui2024bonbon,
title={Bo{NB}oN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling},
author={Lin Gui and Cristina Garbacea and Victor Veitch},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=haSKMlrbX5}
} | This paper concerns the problem of aligning samples from large language models to human preferences using *best-of-$n$* sampling, where we draw $n$ samples, rank them, and return the best one. We consider two fundamental problems. First: what is the relationship between best-of-$n$ and other (RLHF-type) approaches to aligning LLMs? In particular, when should one be preferred to the other? We show that the best-of-$n$ sampling distribution is essentially equivalent to the policy learned by RLHF if we apply a particular monotone transformation to the reward function. Moreover, we show that this transformation yields the best possible trade-off between win-rate against the base model vs KL distance from the base model. Then, best-of-$n$ is a Pareto-optimal win-rate vs KL solution.
The second problem we consider is how to fine-tune a model to mimic the best-of-$n$ sampling distribution, to avoid drawing $n$ samples for each inference. We derive *BonBon Alignment* as a method for achieving this. Experiments show that BonBon alignment yields a model that achieves high win rates while minimally affecting off-target aspects of the generations. | BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling | [
"Lin Gui",
"Cristina Garbacea",
"Victor Veitch"
] | NeurIPS.cc/2024/Conference | 2406.00832 | [
""
] | https://huggingface.co/papers/2406.00832 | 0 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=hYjRmGqq5e | @inproceedings{
qing2024apo,
title={A2{PO}: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective},
author={Yunpeng Qing and Shunyu Liu and Jingyuan Cong and Kaixuan Chen and Yihe Zhou and Mingli Song},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hYjRmGqq5e}
} | Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the out-of-distribution problem. However, existing works often suffer from the constraint conflict issue when offline datasets are collected from multiple behavior policies, i.e., different behavior policies may exhibit inconsistent actions with distinct returns across the state space. To remedy this issue, recent advantage-weighted methods prioritize samples with high advantage values for agent training while inevitably ignoring the diversity of behavior policy. In this paper, we introduce a novel Advantage-Aware Policy Optimization (A2PO) method to explicitly construct advantage-aware policy constraints for offline learning under mixed-quality datasets. Specifically, A2PO employs a conditional variational auto-encoder to disentangle the action distributions of intertwined behavior policies by modeling the advantage values of all training data as conditional variables. Then the agent can follow such disentangled action distribution constraints to optimize the advantage-aware policy towards high advantage values. Extensive experiments conducted on both the single-quality and mixed-quality datasets of the D4RL benchmark demonstrate that A2PO yields results superior to the counterparts. Our code is available at https://github.com/Plankson/A2PO. | A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective | [
"Yunpeng Qing",
"Shunyu Liu",
"Jingyuan Cong",
"Kaixuan Chen",
"Yihe Zhou",
"Mingli Song"
] | NeurIPS.cc/2024/Conference | 2403.07262 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hYMxyeyEc5 | @inproceedings{
wang2024embedding,
title={Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning},
author={Yiming Wang and Pei Zhang and Baosong Yang and Derek F. Wong and Zhuosheng Zhang and Rui Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hYMxyeyEc5}
} | Real-world data deviating from the independent and identically distributed (\textit{i.i.d.}) assumption of in-distribution training data poses security threats to deep networks, thus advancing out-of-distribution (OOD) detection algorithms. Detection methods in generative language models (GLMs) mainly focus on uncertainty estimation and embedding distance measurement, with the latter proven to be most effective in traditional linguistic tasks like summarization and translation. However, another complex generative scenario mathematical reasoning poses significant challenges to embedding-based methods due to its high-density feature of output spaces, but this feature causes larger discrepancies in the embedding shift trajectory between different samples in latent spaces. Hence, we propose a trajectory-based method TV score, which uses trajectory volatility for OOD detection in mathematical reasoning. Experiments show that our method outperforms all traditional algorithms on GLMs under mathematical reasoning scenarios and can be extended to more applications with high-density features in output spaces, such as multiple-choice questions. | Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning | [
"Yiming Wang",
"Pei Zhang",
"Baosong Yang",
"Derek F. Wong",
"Zhuosheng Zhang",
"Rui Wang"
] | NeurIPS.cc/2024/Conference | 2405.14039 | [
"https://github.com/alsace08/ood-math-reasoning"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hYJOfWfw1P | @inproceedings{
kang2024is,
title={Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and {RL}},
author={Enoch H. Kang and Panganamala Kumar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hYJOfWfw1P}
} | Interactive decision making, encompassing bandits, contextual bandits, and reinforcement learning, has recently been of interest to theoretical studies of experimentation design and recommender system algorithm research. One recent finding in this area is that the well-known Graves-Lai constant being zero is a necessary and sufficient condition for achieving bounded (or constant) regret in interactive decision-making. As this condition may be a strong requirement for many applications, the practical usefulness of pursuing bounded regret has been questioned. In this paper, we show that the condition of the Graves-Lai constant being zero is also necessary for a consistent algorithm to achieve delay model robustness when reward delays are unknown (i.e., when feedback is anonymous). Here, model robustness is measured in terms of $\epsilon$-robustness, one of the most widely used and one of the least adversarial robustness concepts in the robust statistics literature. In particular, we show that $\epsilon$-robustness cannot be achieved for a consistent (i.e., uniformly sub-polynomial regret) algorithm, however small the nonzero $\epsilon$ value is, when the Grave-Lai constant is not zero. While this is a strongly negative result, we also provide a positive result for linear rewards models (contextual linear bandits, reinforcement learning with linear MDP) that the Grave-Lai constant being zero is also sufficient for achieving bounded regret without any knowledge of delay models, i.e., the best of both the efficiency world and the delay robustness world. | Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL | [
"Enoch H. Kang",
"Panganamala Kumar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hXgLvYsG2c | @inproceedings{
yang2024comera,
title={Co{MERA}: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization},
author={Zi Yang and Ziyue Liu and Samridhi Choudhary and Xinfeng Xie and Cao Gao and Siegfried Kunzmann and Zheng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hXgLvYsG2c}
} | Training large AI models such as LLMs and DLRMs costs massive GPUs and computing time. The high training cost has become only affordable to big tech companies, meanwhile also causing increasing concerns about the environmental impact. This paper presents CoMERA, a **Co**mputing- and **M**emory-**E**fficient training method via **R**ank-**A**daptive tensor optimization. CoMERA achieves end-to-end rank-adaptive tensor-compressed training via a multi-objective optimization formulation, and improves the training to provide both a high compression ratio and excellent accuracy in the training process. Our optimized numerical computation (e.g., optimized tensorized embedding and tensor-vector contractions) and GPU implementation eliminate part of the run-time overhead in the tensorized training on GPU. This leads to, for the first time, $2-3\times$ speedup per training epoch compared with standard training. CoMERA also outperforms the recent GaLore in terms of both memory and computing efficiency. Specifically, CoMERA is $2\times$ faster per training epoch and $9\times$ more memory-efficient than GaLore on a tested six-encoder transformer with single-batch training. Our method also shows $\sim 2\times$ speedup than standard pre-training on a BERT-like code-generation LLM while achieving $4.23\times$ compression ratio in pre-training.
With further HPC optimization, CoMERA may reduce the pre-training cost of many other LLMs. An implementation of CoMERA is available at <https://github.com/ziyangjoy/CoMERA>. | CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization | [
"Zi Yang",
"Ziyue Liu",
"Samridhi Choudhary",
"Xinfeng Xie",
"Cao Gao",
"Siegfried Kunzmann",
"Zheng Zhang"
] | NeurIPS.cc/2024/Conference | 2405.14377 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hWRVbdAWiS | @inproceedings{
zhang2024entropyregularized,
title={Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning},
author={Ruoqi Zhang and Ziwei Luo and Jens Sj{\"o}lund and Thomas B. Sch{\"o}n and Per Mattsson},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hWRVbdAWiS}
} | Diffusion policy has shown a strong ability to express complex action distributions in offline reinforcement learning (RL). However, it suffers from overestimating Q-value functions on out-of-distribution (OOD) data points due to the offline dataset limitation. To address it, this paper proposes a novel entropy-regularized diffusion policy and takes into account the confidence of the Q-value prediction with Q-ensembles. At the core of our diffusion policy is a mean-reverting stochastic differential equation (SDE) that transfers the action distribution into a standard Gaussian form and then samples actions conditioned on the environment state with a corresponding reverse-time process. We show that the entropy of such a policy is tractable and that can be used to increase the exploration of OOD samples in offline RL training. Moreover, we propose using the lower confidence bound of Q-ensembles for pessimistic Q-value function estimation. The proposed approach demonstrates state-of-the-art performance across a range of tasks in the D4RL benchmarks, significantly improving upon existing diffusion-based policies. The code is available at https://github.com/ruoqizzz/entropy-offlineRL. | Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning | [
"Ruoqi Zhang",
"Ziwei Luo",
"Jens Sjölund",
"Thomas B. Schön",
"Per Mattsson"
] | NeurIPS.cc/2024/Conference | 2402.04080 | [
"https://github.com/ruoqizzz/entropy-offlineRL"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hW5QWiCctl | @inproceedings{
zhang2024graphmorph,
title={GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs},
author={Zhao Zhang and Ziwei Zhao and Dong Wang and Liwei Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hW5QWiCctl}
} | Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph comprises two main components: a Graph Decoder and a Morph Module. Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch. The Morph Module processes two primary inputs: the graph and the centerline probability map, provided by the Graph Decoder and the segmentation network, respectively. Employing a novel SkeletonDijkstra algorithm, the Morph Module produces a centerline mask that aligns with the predicted graph. Furthermore, we observe that employing centerline masks predicted by GraphMorph significantly reduces false positives in the segmentation task, which is achieved by a simple yet effective post-processing strategy. The efficacy of our method in the centerline extraction and segmentation tasks has been substantiated through experimental evaluations across various datasets. Source code will be released soon. | GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs | [
"Zhao Zhang",
"Ziwei Zhao",
"Dong Wang",
"Liwei Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hVmi98a0ki | @inproceedings{
lohoff2024optimizing,
title={Optimizing Automatic Differentiation with Deep Reinforcement Learning},
author={Jamie Lohoff and Emre Neftci},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hVmi98a0ki}
} | Computing Jacobians with automatic differentiation is ubiquitous in many scientific domains such as machine learning, computational fluid dynamics, robotics and finance.
Even small savings in the number of computations or memory usage in Jacobian computations can already incur massive savings in energy consumption and runtime.
While there exist many methods that allow for such savings, they generally trade computational efficiency for approximations of the exact Jacobian.
In this paper, we present a novel method to optimize the number of necessary multiplications for Jacobian computation by leveraging deep reinforcement learning (RL) and a concept called cross-country elimination while still computing the exact Jacobian.
Cross-country elimination is a framework for automatic differentiation that phrases Jacobian accumulation as ordered elimination of all vertices on the computational graph where every elimination incurs a certain computational cost.
Finding the optimal elimination order that minimizes the number of necessary multiplications can be seen as a single player game which in our case is played by an RL agent.
We demonstrate that this method achieves up to 33% improvements over state-of-the-art methods on several relevant tasks taken from relevant domains.
Furthermore, we show that these theoretical gains translate into actual runtime improvements by providing a cross-country elimination interpreter in JAX that can execute the obtained elimination orders. | Optimizing Automatic Differentiation with Deep Reinforcement Learning | [
"Jamie Lohoff",
"Emre Neftci"
] | NeurIPS.cc/2024/Conference | 2406.05027 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=hVGAGU4TKk | @inproceedings{
wang2024neurodin,
title={NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction},
author={Yifan Wang and Di Huang and Weicai Ye and Guofeng Zhang and Wanli Ouyang and Tong He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hVGAGU4TKk}
} | Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction. Although promising, SDF-based methods often fail to capture detailed geometric structures, resulting in visible defects. By comparing SDF-based volume rendering to density-based volume rendering, we identify two main factors within the SDF-based approach that degrade surface quality: SDF-to-density representation and geometric regularization. These factors introduce challenges that hinder the optimization of the SDF field. To address these issues, we introduce NeuRodin, a novel two-stage neural surface reconstruction framework that not only achieves high-fidelity surface reconstruction but also retains the flexible optimization characteristics of density-based methods.
NeuRodin incorporates innovative strategies that facilitate transformation of arbitrary topologies and reduce artifacts associated with density bias.
Extensive evaluations on the Tanks and Temples and ScanNet++ datasets demonstrate the superiority of NeuRodin, showing strong reconstruction capabilities for both indoor and outdoor environments using solely posed RGB captures. Project website:
https://open3dvlab.github.io/NeuRodin/ | NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction | [
"Yifan Wang",
"Di Huang",
"Weicai Ye",
"Guofeng Zhang",
"Wanli Ouyang",
"Tong He"
] | NeurIPS.cc/2024/Conference | 2408.10178 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hUGD1aNMrp | @inproceedings{
chen2024assouad,
title={Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability},
author={Fan Chen and Dylan J Foster and Yanjun Han and Jian Qian and Alexander Rakhlin and Yunbei Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hUGD1aNMrp}
} | We develop a unifying framework for information-theoretic lower bound in statistical estimation and interactive decision making. Classical lower bound techniques---such as Fano's inequality, Le Cam's method, and Assouad's lemma---are central to the study of minimax risk in statistical estimation, yet are insufficient to provide tight lower bounds for \emph{interactive decision making} algorithms that collect data interactively (e.g., algorithms for bandits and reinforcement learning). Recent work of Foster et al. provides minimax lower bounds for interactive decision making using seemingly different analysis techniques from the classical methods. These results---which are proven using a complexity measure known as the \emph{Decision-Estimation Coefficient} (DEC)---capture difficulties unique to interactive learning, yet do not recover the tightest known lower bounds for passive estimation. We propose a unified view of these distinct methodologies through a new lower bound approach called \emph{interactive Fano method}. As an application, we introduce a novel complexity measure, the \emph{Decision Dimension}, which facilitates the new lower bounds for interactive decision making that extend the DEC methodology by incorporating the complexity of estimation. Using the Decision Dimension, we (i) provide a unified characterization of learnability for \emph{any} structured bandit problem, (ii) close the remaining gap between the upper and lower bounds in Foster et al. (up to polynomial factors) for any interactive decision making problem in which the underlying model class is convex. | Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability | [
"Fan Chen",
"Dylan J Foster",
"Yanjun Han",
"Jian Qian",
"Alexander Rakhlin",
"Yunbei Xu"
] | NeurIPS.cc/2024/Conference | 2410.05117 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=hT4y7D2o2T | @inproceedings{
xi2024propensity,
title={Propensity Score Alignment of Unpaired Multimodal Data},
author={Johnny Xi and Jana Osea and Zuheng Xu and Jason Hartford},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hT4y7D2o2T}
} | Multimodal representation learning techniques typically require paired samples to learn shared representations, but collecting paired samples can be challenging in fields like biology, where measurement devices often destroy the samples. This paper presents an approach to address the challenge of aligning unpaired samples across disparate modalities in multimodal representation learning. We draw an analogy between potential outcomes in causal inference and potential views in multimodal observations, allowing us to leverage Rubin's framework to estimate a common space for matching samples. Our approach assumes experimentally perturbed samples by treatments, and uses this to estimate a propensity score from each modality. We show that the propensity score encapsulates all shared information between a latent state and treatment, and can be used to define a distance between samples. We experiment with two alignment techniques that leverage this distance---shared nearest neighbours (SNN) and optimal transport (OT) matching---and find that OT matching results in significant improvements over state-of-the-art alignment approaches in on synthetic multi-modal tasks, in real-world data from NeurIPS Multimodal Single-Cell Integration Challenge, and on a single cell microscopy to expression prediction task. | Propensity Score Alignment of Unpaired Multimodal Data | [
"Johnny Xi",
"Jana Osea",
"Zuheng Xu",
"Jason Hartford"
] | NeurIPS.cc/2024/Conference | 2404.01595 | [
"https://github.com/valence-labs/prop-score-pairing"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hS1jvV3Dk3 | @inproceedings{
hu2024localized,
title={Localized Zeroth-Order Prompt Optimization},
author={Wenyang Hu and Yao Shu and Zongmin Yu and Zhaoxuan Wu and Xiaoqiang Lin and Zhongxiang Dai and See-Kiong Ng and Bryan Kian Hsiang Low},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hS1jvV3Dk3}
} | The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of black-box LLMs. Existing methodologies usually prioritize a global optimization for finding the global optimum, which however will perform poorly in certain tasks. This thus motivates us to re-think the necessity of finding a global optimum in prompt optimization. To answer this, we conduct a thorough empirical study on prompt optimization and draw two major insights. Contrasting with the rarity of global optimum, local optima are usually prevalent and well-performed, which can be more worthwhile for efficient prompt optimization (**Insight I**). The choice of the input domain, covering both the generation and the representation of prompts, affects the identification of well-performing local optima (**Insight II**). Inspired by these insights, we propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO), which incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency, which we demonstrate through extensive experiments. | Localized Zeroth-Order Prompt Optimization | [
"Wenyang Hu",
"Yao Shu",
"Zongmin Yu",
"Zhaoxuan Wu",
"Xiaoqiang Lin",
"Zhongxiang Dai",
"See-Kiong Ng",
"Bryan Kian Hsiang Low"
] | NeurIPS.cc/2024/Conference | 2403.02993 | [
""
] | https://huggingface.co/papers/2403.02993 | 2 | 0 | 0 | 8 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=hRqaot0NZF | @inproceedings{
liu2024less,
title={{LESS}: Label-Efficient and Single-Stage Referring 3D Segmentation},
author={Xuexun Liu and Xiaoxu Xu and Jinlong Li and Qiudan Zhang and Xu Wang and Nicu Sebe and Lin Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hRqaot0NZF}
} | Referring 3D Segmentation is a visual-language task that segments all points of the specified object from a 3D point cloud described by a sentence of query. Previous works perform a two-stage paradigm, first conducting language-agnostic instance segmentation then matching with given text query. However, the semantic concepts from text query and visual cues are separately interacted during the training, and both instance and semantic labels for each object are required, which is time consuming and human-labor intensive. To mitigate these issues, we propose a novel Referring 3D Segmentation pipeline, Label-Efficient and Single-Stage, dubbed LESS, which is only under the supervision of efficient binary mask. Specifically, we design a Point-Word Cross-Modal Alignment module for aligning the fine-grained features of points and textual embedding. Query Mask Predictor module and Query-Sentence Alignment module are introduced for coarse-grained alignment between masks and query. Furthermore, we propose an area regularization loss, which coarsely reduces irrelevant background predictions on a large scale. Besides, a point-to-point contrastive loss is proposed concentrating on distinguishing points with subtly similar features. Through extensive experiments, we achieve state-of-the-art performance on ScanRefer dataset by surpassing the previous methods about 3.7% mIoU using only binary labels. Code is available at https://github.com/mellody11/LESS. | LESS: Label-Efficient and Single-Stage Referring 3D Segmentation | [
"Xuexun Liu",
"Xiaoxu Xu",
"Jinlong Li",
"Qiudan Zhang",
"Xu Wang",
"Nicu Sebe",
"Lin Ma"
] | NeurIPS.cc/2024/Conference | 2410.13294 | [
"https://github.com/mellody11/less"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hRKsahifqj | @inproceedings{
winkel2024autoregressive,
title={Autoregressive Policy Optimization for Constrained Allocation Tasks},
author={David Winkel and Niklas Alexander Strau{\ss} and Maximilian Bernhard and Zongyue Li and Thomas Seidl and Matthias Schubert},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hRKsahifqj}
} | Allocation tasks represent a class of problems where a limited amount of resources must be allocated to a set of entities at each time step. Prominent examples of this task include portfolio optimization or distributing computational workloads across servers.
Allocation tasks are typically bound by linear constraints describing practical requirements that have to be strictly fulfilled at all times. In portfolio optimization, for example, investors may be obligated to allocate less than 30\% of the funds into a certain industrial sector in any investment period.
Such constraints restrict the action space of allowed allocations in intricate ways, which makes learning a policy that avoids constraint violations difficult.
In this paper, we propose a new method for constrained allocation tasks based on an autoregressive process to sequentially sample allocations for each entity. In addition, we introduce a novel de-biasing mechanism to counter the initial bias caused by sequential sampling. We demonstrate the superior performance of our approach compared to a variety of Constrained Reinforcement Learning (CRL) methods on three distinct constrained allocation tasks: portfolio optimization, computational workload distribution, and a synthetic allocation benchmark. Our code is available at: https://github.com/niklasdbs/paspo | Autoregressive Policy Optimization for Constrained Allocation Tasks | [
"David Winkel",
"Niklas Alexander Strauß",
"Maximilian Bernhard",
"Zongyue Li",
"Thomas Seidl",
"Matthias Schubert"
] | NeurIPS.cc/2024/Conference | 2409.18735 | [
"https://github.com/niklasdbs/paspo"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hQfcrTBHeD | @inproceedings{
mendler-d{\"u}nner2024an,
title={An engine not a camera: Measuring performative power of online search},
author={Celestine Mendler-D{\"u}nner and Gabriele Carovano and Moritz Hardt},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hQfcrTBHeD}
} | The power of digital platforms is at the center of major ongoing policy and regulatory efforts. To advance existing debates, we designed and executed an experiment to measure the performative power of online search providers. Instantiated in our setting, performative power quantifies the ability of a search engine to steer web traffic by rearranging results. To operationalize this definition we developed a browser extension that performs unassuming randomized experiments in the background. These randomized experiments emulate updates to the search algorithm and identify the causal effect of different content arrangements on clicks. Analyzing tens of thousands of clicks, we discuss what our robust quantitative findings say about the power of online search engines, using the Google Shopping antitrust investigation as a case study. More broadly, we envision our work to serve as a blueprint for how the recent definition of performative power can help integrate quantitative insights from online experiments with future investigations into the economic power of digital platforms. | An engine not a camera: Measuring performative power of online search | [
"Celestine Mendler-Dünner",
"Gabriele Carovano",
"Moritz Hardt"
] | NeurIPS.cc/2024/Conference | 2405.19073 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hQJksiskaa | @inproceedings{
deng2024autobidders,
title={Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency},
author={Yuan Deng and Jieming Mao and Vahab Mirrokni and Hanrui Zhang and Song Zuo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hQJksiskaa}
} | The recent increasing adoption of autobidding has inspired the growing interest in analyzing the performance of classic mechanism with value-maximizing autobidders both theoretically and empirically. It is known that optimal welfare can be obtained in first-price auctions if autobidders are restricted to uniform bid-scaling and the price of anarchy is $2$ when non-uniform bid-scaling strategies are allowed.
In this paper, we provide a fine-grained price of anarchy analysis for non-uniform bid-scaling strategies in first-price auctions, demonstrating the reason why more powerful (individual) non-uniform bid-scaling strategies may lead to worse (aggregated) performance in social welfare. Our theoretical results match recent empirical findings that a higher level of non-uniform bid-scaling leads to lower welfare performance in first-price auctions. | Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency | [
"Yuan Deng",
"Jieming Mao",
"Vahab Mirrokni",
"Hanrui Zhang",
"Song Zuo"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hOcsUrOY0D | @inproceedings{
kulynych2024attackaware,
title={Attack-Aware Noise Calibration for Differential Privacy},
author={Bogdan Kulynych and Juan Felipe Gomez and Georgios Kaissis and Flavio Calmon and Carmela Troncoso},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hOcsUrOY0D}
} | Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to limit the risk of information leakage. The scale of the added noise is critical, as it determines the trade-off between privacy and utility. The standard practice is to select the noise scale to satisfy a given privacy budget ε. This privacy budget is in turn interpreted in terms of operational attack risks, such as accuracy, sensitivity, and specificity of inference attacks aimed to recover
information about the training data records. We show that first calibrating the noise scale to a privacy budget ε, and then translating ε to attack risk leads to overly conservative risk assessments and unnecessarily low utility. Instead, we propose methods to directly calibrate the noise scale to a desired attack risk level, bypassing the step of choosing ε. For a given notion of attack risk, our approach significantly
decreases noise scale, leading to increased utility at the same level of privacy. We empirically demonstrate that calibrating noise to attack sensitivity/specificity, rather than ε, when training privacy-preserving ML models substantially improves model accuracy for the same risk level. Our work provides a principled and practical way to improve the utility of privacy-preserving ML without compromising on privacy. | Attack-Aware Noise Calibration for Differential Privacy | [
"Bogdan Kulynych",
"Juan Felipe Gomez",
"Georgios Kaissis",
"Flavio Calmon",
"Carmela Troncoso"
] | NeurIPS.cc/2024/Conference | 2407.02191 | [
"https://github.com/bogdan-kulynych/riskcal"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hNlk9cIGo9 | @inproceedings{
lowy2024faster,
title={Faster Algorithms for User-Level Private Stochastic Convex Optimization},
author={Andrew Lowy and Daogao Liu and Hilal Asi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hNlk9cIGo9}
} | We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are $n$ users (e.g., cell phones), each possessing $m$ data items (e.g., text messages), and we need to protect the privacy of each user's entire collection of data items. Existing algorithms for user-level DP SCO are impractical in many large-scale machine learning scenarios because: (i) they make restrictive assumptions on the smoothness parameter of the loss function and require the number of users to grow polynomially with the dimension of the parameter space; or (ii) they are prohibitively slow, requiring at least $(mn)^{3/2}$ gradient computations for smooth losses and $(mn)^3$ computations for non-smooth losses. To address these limitations, we provide novel user-level DP algorithms with state-of-the-art excess risk and runtime guarantees, without stringent assumptions. First, we develop a linear-time algorithm with state-of-the-art excess risk (for a non-trivial linear-time algorithm) under a mild smoothness assumption. Our second algorithm applies to arbitrary smooth losses and achieves optimal excess risk in $\approx (mn)^{9/8}$ gradient computations. Third, for non-smooth loss functions, we obtain optimal excess risk in $n^{11/8} m^{5/4}$ gradient computations. Moreover, our algorithms do not require the number of users to grow polynomially with the dimension. | Faster Algorithms for User-Level Private Stochastic Convex Optimization | [
"Andrew Lowy",
"Daogao Liu",
"Hilal Asi"
] | NeurIPS.cc/2024/Conference | 2410.18391 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hLoiXOzoly | @inproceedings{
brookes2024contrastive,
title={Contrastive losses as generalized models of global epistasis},
author={David H Brookes and Jakub Otwinowski and Sam Sinai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hLoiXOzoly}
} | Fitness functions map large combinatorial spaces of biological sequences to properties of interest. Inferring these multimodal functions from experimental data is a central task in modern protein engineering. Global epistasis models are an effective and physically-grounded class of models for estimating fitness functions from observed data. These models assume that a sparse latent function is transformed by a monotonic nonlinearity to emit measurable fitness. Here we demonstrate that minimizing supervised contrastive loss functions, such as the Bradley-Terry loss, is a simple and flexible technique for extracting the sparse latent function implied by global epistasis. We argue by way of a fitness-epistasis uncertainty principle that the nonlinearities in global epistasis models can produce observed fitness functions that do not admit sparse representations, and thus may be inefficient to learn from observations when using a Mean Squared Error (MSE) loss (a common practice). We show that contrastive losses are able to accurately estimate a ranking function from limited data even in regimes where MSE is ineffective and validate the practical utility of this insight by demonstrating that contrastive loss functions result in consistently improved performance on empirical benchmark tasks. | Contrastive losses as generalized models of global epistasis | [
"David H Brookes",
"Jakub Otwinowski",
"Sam Sinai"
] | NeurIPS.cc/2024/Conference | 2305.03136 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hKloKv7pR2 | @inproceedings{
asadulaev2024rethinking,
title={Rethinking Optimal Transport in Offline Reinforcement Learning},
author={Arip Asadulaev and Rostislav Korst and Alexander Korotin and Vage Egiazarian and Andrey Filchenkov and Evgeny Burnaev},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hKloKv7pR2}
} | We propose a novel algorithm for offline reinforcement learning using optimal transport. Typically, in offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is necessary to \emph{stitch} the best behaviors from the dataset. To address this problem, we rethink offline reinforcement learning as an optimal transportation problem. And based on this, we present an algorithm that aims to find a policy that maps states to a \emph{partial} distribution of the best expert actions for each given state. We evaluate the performance of our algorithm on continuous control problems from the D4RL suite and demonstrate improvements over existing methods. | Rethinking Optimal Transport in Offline Reinforcement Learning | [
"Arip Asadulaev",
"Rostislav Korst",
"Alexander Korotin",
"Vage Egiazarian",
"Andrey Filchenkov",
"Evgeny Burnaev"
] | NeurIPS.cc/2024/Conference | 2410.14069 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hKcx2wa3P0 | @inproceedings{
wang2024on,
title={On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity},
author={Chao Wang and Xin HE and Yuwen Wang and Junhui Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hKcx2wa3P0}
} | This paper investigates the impact of alignment between the target function of interest and the kernel matrix on a variety of kernel-based methods based on a general loss belonging to a rich loss function family, which covers many commonly used methods in regression and classification problems. We consider the truncated kernel-based method (TKM) which is estimated within a reduced function space constructed by using the spectral truncation of the kernel matrix and compare its theoretical behavior to that of the standard kernel-based method (KM) under various settings. By using the kernel complexity function that quantifies the complexity of the induced function space, we derive the upper bounds for both TKM and KM, and further reveal their dependencies on the degree of target-kernel alignment. Specifically, for the alignment with polynomial decay, the established results indicate that under the just-aligned and weakly-aligned regimes, TKM and KM share the same learning rate. Yet, under the strongly-aligned regime, KM suffers the saturation effect, while TKM can be continuously improved as the alignment becomes stronger. This further implies that TKM has a strong ability to capture the strong alignment and provide a theoretically guaranteed solution to eliminate the phenomena of saturation effect. The minimax lower bound is also established for the squared loss to confirm the optimality of TKM. Extensive numerical experiments further support our theoretical findings. The Python code for reproducing the numerical experiments is available at https://github.com/wywangen. | On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity | [
"Chao Wang",
"Xin HE",
"Yuwen Wang",
"Junhui Wang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hKVTwQQu76 | @inproceedings{
zhao2024dfagnn,
title={{DFA}-{GNN}: Forward Learning of Graph Neural Networks by Direct Feedback Alignment},
author={Gongpei Zhao and Tao Wang and Congyan Lang and Yi Jin and Yidong Li and Haibin Ling},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hKVTwQQu76}
} | Graph neural networks (GNNs) are recognized for their strong performance across various applications, with the backpropagation (BP) algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-backpropagation (non-BP) training algorithms, such as the direct feedback alignment (DFA), have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the independent and identically distributed (i.i.d.) assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks. | DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment | [
"Gongpei Zhao",
"Tao Wang",
"Congyan Lang",
"Yi Jin",
"Yidong Li",
"Haibin Ling"
] | NeurIPS.cc/2024/Conference | 2406.02040 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hK7XTpCtBi | @inproceedings{
cai2024fast,
title={Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms},
author={Yang Cai and Gabriele Farina and Julien Grand-Cl{\'e}ment and Christian Kroer and Chung-Wei Lee and Haipeng Luo and Weiqiang Zheng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hK7XTpCtBi}
} | Self play via online learning is one of the premier ways to solve large-scale zero-sum games, both in theory and practice. Particularly popular algorithms include optimistic multiplicative weights update (OMWU) and optimistic gradient-descent-ascent (OGDA). While both algorithms enjoy $O(1/T)$ ergodic convergence to Nash equilibrium in two-player zero-sum games, OMWU offers several advantages, including logarithmic dependence on the size of the payoff matrix and $\tilde{O}(1/T)$ convergence to coarse correlated equilibria even in general-sum games. However, in terms of last-iterate convergence in two-player zero-sum games, an increasingly popular topic in this area, OGDA guarantees that the duality gap shrinks at a rate of $(1/\sqrt{T})$, while the best existing last-iterate convergence for OMWU depends on some game-dependent constant that could be arbitrarily large. This begs the question: is this potentially slow last-iterate convergence an inherent disadvantage of OMWU, or is the current analysis too loose? Somewhat surprisingly, we show that the former is true. More generally, we prove that a broad class of algorithms that do not forget the past quickly all suffer the same issue: for any arbitrarily small $\delta>0$, there exists a $2\times 2$ matrix game such that the algorithm admits a constant duality gap even after $1/\delta$ rounds. This class of algorithms includes OMWU and other standard optimistic follow-the-regularized-leader algorithms. | Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms | [
"Yang Cai",
"Gabriele Farina",
"Julien Grand-Clément",
"Christian Kroer",
"Chung-Wei Lee",
"Haipeng Luo",
"Weiqiang Zheng"
] | NeurIPS.cc/2024/Conference | 2406.10631 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hH4bPkOhhh | @inproceedings{
qiu2024identifying,
title={Identifying Selections for Unsupervised Subtask Discovery},
author={Yiwen Qiu and Yujia Zheng and Kun Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hH4bPkOhhh}
} | When solving long-horizon tasks, it is intriguing to decompose the high-level task into subtasks. Decomposing experiences into reusable subtasks can improve data efficiency, accelerate policy generalization, and in general provide promising solutions to multi-task reinforcement learning and imitation learning problems. However, the concept of subtasks is not sufficiently understood and modeled yet, and existing works often overlook the true structure of the data generation process: subtasks are the results of a *selection* mechanism on actions, rather than possible underlying confounders or intermediates. Specifically, we provide a theory to identify, and experiments to verify the existence of selection variables in such data. These selections serve as subgoals that indicate subtasks and guide policy. In light of this idea, we develop a sequential non-negative matrix factorization (seq- NMF) method to learn these subgoals and extract meaningful behavior patterns as subtasks. Our empirical results on a challenging Kitchen environment demonstrate that the learned subtasks effectively enhance the generalization to new tasks in multi-task imitation learning scenarios. The codes are provided at this [*link*](https://anonymous.4open.science/r/Identifying\_Selections\_for\_Unsupervised\_Subtask\_Discovery/README.md). | Identifying Selections for Unsupervised Subtask Discovery | [
"Yiwen Qiu",
"Yujia Zheng",
"Kun Zhang"
] | NeurIPS.cc/2024/Conference | 2410.21616 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hGgkdFF2hR | @inproceedings{
halmos2024lowrank,
title={Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling},
author={Peter Halmos and Xinhao Liu and Julian Gold and Benjamin Raphael},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hGgkdFF2hR}
} | Optimal transport (OT) is a general framework for finding a minimum-cost transport plan, or coupling, between probability distributions, and has many applications in machine learning. A key challenge in applying OT to massive datasets is the quadratic scaling of the coupling matrix with the size of the dataset. [Forrow et al. 2019] introduced a factored coupling for the k-Wasserstein barycenter problem, which [Scetbon et al. 2021] adapted to solve the primal low-rank OT problem. We derive an alternative parameterization of the low-rank problem based on the _latent coupling_ (LC) factorization previously introduced by [Lin et al. 2021] generalizing [Forrow et al. 2019]. The LC factorization has multiple advantages for low-rank OT including decoupling the problem into three OT problems and greater flexibility and interpretability. We leverage these advantages to derive a new algorithm _Factor Relaxation with Latent Coupling_ (FRLC), which uses _coordinate_ mirror descent to compute the LC factorization. FRLC handles multiple OT objectives (Wasserstein, Gromov-Wasserstein, Fused Gromov-Wasserstein), and marginal constraints (balanced, unbalanced, and semi-relaxed) with linear space complexity. We provide theoretical results on FRLC, and demonstrate superior performance on diverse applications -- including graph clustering and spatial transcriptomics -- while demonstrating its interpretability. | Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling | [
"Peter Halmos",
"Xinhao Liu",
"Julian Gold",
"Benjamin Raphael"
] | NeurIPS.cc/2024/Conference | 2411.10555 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hFTye9Ge40 | @inproceedings{
jang2024fixed,
title={Fixed Confidence Best Arm Identification in the Bayesian Setting},
author={Kyoungseok Jang and Junpei Komiyama and Kazutoshi Yamazaki},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hFTye9Ge40}
} | We consider the fixed-confidence best arm identification (FC-BAI) problem in the Bayesian setting. This problem aims to find the arm of the largest mean with a fixed confidence level when the bandit model has been sampled from the known prior.
Most studies on the FC-BAI problem have been conducted in the frequentist setting, where the bandit model is predetermined before the game starts.
We show that the traditional FC-BAI algorithms studied in the frequentist setting, such as track-and-stop and top-two algorithms, result in arbitrarily suboptimal performances in the Bayesian setting.
We also obtain a lower bound of the expected number of samples in the Bayesian setting and introduce a variant of successive elimination that has a matching performance with the lower bound up to a logarithmic factor. Simulations verify the theoretical results. | Fixed Confidence Best Arm Identification in the Bayesian Setting | [
"Kyoungseok Jang",
"Junpei Komiyama",
"Kazutoshi Yamazaki"
] | NeurIPS.cc/2024/Conference | 2402.10429 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hF6vatntqc | @inproceedings{
kim2024transformers,
title={Transformers are Minimax Optimal Nonparametric In-Context Learners},
author={Juno Kim and Tai Nakamaki and Taiji Suzuki},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hF6vatntqc}
} | In-context learning (ICL) of large language models has proven to be a surprisingly effective method of learning a new task from only a few demonstrative examples. In this paper, we shed light on the efficacy of ICL from the viewpoint of statistical learning theory. We develop approximation and generalization error analyses for a transformer model composed of a deep neural network and one linear attention layer, pretrained on nonparametric regression tasks sampled from general function spaces including the Besov space and piecewise $\gamma$-smooth class. In particular, we show that sufficiently trained transformers can achieve -- and even improve upon -- the minimax optimal estimation risk in context by encoding the most relevant basis representations during pretraining. Our analysis extends to high-dimensional or sequential data and distinguishes the \emph{pretraining} and \emph{in-context} generalization gaps, establishing upper and lower bounds w.r.t. both the number of tasks and in-context examples. These findings shed light on the effectiveness of few-shot prompting and the roles of task diversity and representation learning for ICL. | Transformers are Minimax Optimal Nonparametric In-Context Learners | [
"Juno Kim",
"Tai Nakamaki",
"Taiji Suzuki"
] | NeurIPS.cc/2024/Conference | 2408.12186 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hEKSSsv5Q9 | @inproceedings{
zeng2024dlad,
title={{DLAD}: Improving Logits-based Detector without Logits from Black-box {LLM}s},
author={Cong Zeng and Shengkun Tang and Xianjun Yang and Yuanzhou Chen and Yiyou Sun and zhiqiang xu and Yao Li and Haifeng Chen and Wei Cheng and Dongkuan Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hEKSSsv5Q9}
} | The advent of Large Language Models (LLMs) has revolutionized text generation, producing outputs that closely mimic human writing. This blurring of lines between machine- and human-written text presents new challenges in distinguishing one from the other – a task further complicated by the frequent updates and closed nature of leading proprietary LLMs. Traditional logits-based detection methods leverage surrogate models for identifying LLM-generated content when the exact logits are unavailable from black-box LLMs. However, these methods grapple with the misalignment between the distributions of the surrogate and the often undisclosed target models, leading to performance degradation, particularly with the introduction of new, closed-source models. Furthermore, while current methodologies are generally effective when the source model is identified, they falter in scenarios where the model version remains unknown, or the test set comprises outputs from various source models. To address these limitations, we present \textbf{D}istribution-\textbf{A}ligned \textbf{L}LMs \textbf{D}etection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection even without logits from source LLMs. DALD is designed to align the surrogate model's distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations with minimal training investment. By leveraging corpus samples from publicly accessible outputs of advanced models such as ChatGPT, GPT-4 and Claude-3, DALD fine-tunes surrogate models to synchronize with unknown source model distributions effectively. Our approach achieves SOTA performance in black-box settings on different advanced closed-source and open-source models. The versatility of our method enriches widely adopted zero-shot detection frameworks (DetectGPT, DNA-GPT, Fast-DetectGPT) with a `plug-and-play' enhancement feature.
Extensive experiments validate that our methodology reliably secures high detection precision for LLM-generated text and effectively detects text from diverse model origins through a singular detector.
Our method is also robust under the revised text attack and non-English texts. | DLAD: Improving Logits-based Detector without Logits from Black-box LLMs | [
"Cong Zeng",
"Shengkun Tang",
"Xianjun Yang",
"Yuanzhou Chen",
"Yiyou Sun",
"zhiqiang xu",
"Yao Li",
"Haifeng Chen",
"Wei Cheng",
"Dongkuan Xu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hE6ZxU0N3c | @inproceedings{
choi2024understanding,
title={Understanding Multi-Granularity for Open-Vocabulary Part Segmentation},
author={Jiho Choi and Seonho Lee and Seungho Lee and Minhyun Lee and Hyunjung Shim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hE6ZxU0N3c}
} | Open-vocabulary part segmentation (OVPS) is an emerging research area focused on segmenting fine-grained entities using diverse and previously unseen vocabularies.
Our study highlights the inherent complexities of part segmentation due to intricate boundaries and diverse granularity, reflecting the knowledge-based nature of part identification.
To address these challenges, we propose PartCLIPSeg, a novel framework utilizing generalized parts and object-level contexts to mitigate the lack of generalization in fine-grained parts.
PartCLIPSeg integrates competitive part relationships and attention control, alleviating ambiguous boundaries and underrepresented parts.
Experimental results demonstrate that PartCLIPSeg outperforms existing state-of-the-art OVPS methods, offering refined segmentation and an advanced understanding of part relationships within images.
Through extensive experiments, our model demonstrated a significant improvement over the state-of-the-art models on the Pascal-Part-116, ADE20K-Part-234, and PartImageNet datasets.
Our code is available at https://github.com/kaist-cvml/part-clipseg. | Understanding Multi-Granularity for Open-Vocabulary Part Segmentation | [
"Jiho Choi",
"Seonho Lee",
"Seungho Lee",
"Minhyun Lee",
"Hyunjung Shim"
] | NeurIPS.cc/2024/Conference | 2406.11384 | [
"https://github.com/kaist-cvml-lab/part-clipseg"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hD9TUV4xdz | @inproceedings{
li2024surge,
title={Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling},
author={Shuaipeng Li and Penghao Zhao and Hailin Zhang and Samm Sun and Hao Wu and Dian Jiao and Weiyan Wang and Chengjun Liu and Zheng Fang and Jinbao Xue and Yangyu Tao and Bin CUI and Di Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hD9TUV4xdz}
} | In current deep learning tasks, Adam-style optimizers—such as Adam, Adagrad, RMSprop, Adafactor, and Lion—have been widely used as alternatives to SGD-style optimizers. These optimizers typically update model parameters using the sign of gradients, resulting in more stable convergence curves.
The learning rate and the batch size are the most critical hyperparameters for optimizers, which require careful tuning to enable effective convergence. Previous research has shown that the optimal learning rate increases linearly (or follows similar rules) with batch size for SGD-style optimizers. However, this conclusion is not applicable to Adam-style optimizers.
In this paper, we elucidate the connection between optimal learning rates and batch sizes for Adam-style optimizers through both theoretical analysis and extensive experiments.
First, we raise the scaling law between batch sizes and optimal learning rates in the “sign of gradient” case, in which we prove that the optimal learning rate first rises and then falls as the batch size increases. Moreover, the peak value of the surge will gradually move toward the larger batch size as training progresses.
Second, we conduct experiments on various CV and NLP tasks and verify the correctness of the scaling law. | Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling | [
"Shuaipeng Li",
"Penghao Zhao",
"Hailin Zhang",
"Samm Sun",
"Hao Wu",
"Dian Jiao",
"Weiyan Wang",
"Chengjun Liu",
"Zheng Fang",
"Jinbao Xue",
"Yangyu Tao",
"Bin CUI",
"Di Wang"
] | NeurIPS.cc/2024/Conference | 2405.14578 | [
""
] | https://huggingface.co/papers/2405.14578 | 0 | 0 | 0 | 13 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=hD8Et4uZ1o | @inproceedings{
jacobsen2024an,
title={An Equivalence Between Static and Dynamic Regret Minimization},
author={Andrew Jacobsen and Francesco Orabona},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hD8Et4uZ1o}
} | We study the problem of dynamic regret minimization in online convex optimization, in which the objective is to minimize the difference between the cumulative loss of an algorithm and that of an arbitrary sequence of comparators. While the literature on this topic is very rich, a unifying framework for the analysis and design of these algorithms is still missing. In this paper we show that /for linear losses, dynamic regret minimization is equivalent to static regret minimization in an extended decision space/. Using this simple observation, we show that there is a frontier of lower bounds trading off penalties due to the variance of the losses and penalties due to variability of the comparator sequence, and provide a framework for achieving any of the guarantees along this frontier. As a result, we also prove for the first time that adapting to the squared path-length of an arbitrary sequence of comparators to achieve regret $R_{T}(u_{1},\dots,u_{T})\le O(\sqrt{T\sum_{t} \\|u_{t}-u_{t+1}\\|^{2}})$ is impossible. However, using our framework we introduce an alternative notion of variability based on a locally-smoothed comparator sequence $\bar u_{1}, \dots, \bar u_{T}$, and provide an algorithm guaranteeing dynamic regret of the form $R_{T}(u_{1},\dots,u_{T})\le \tilde O(\sqrt{T\sum_{i}\\|\bar u_{i}-\bar u_{i+1}\\|^{2}})$, while still matching in the worst case the usual path-length dependencies up to polylogarithmic terms. | An Equivalence Between Static and Dynamic Regret Minimization | [
"Andrew Jacobsen",
"Francesco Orabona"
] | NeurIPS.cc/2024/Conference | 2406.01577 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hCOuip5Ona | @inproceedings{
holtz2024continuous,
title={Continuous Partitioning for Graph-Based Semi-Supervised Learning},
author={Chester Holtz and Pengwen Chen and Zhengchao Wan and Chung-Kuan Cheng and Gal Mishne},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hCOuip5Ona}
} | Laplace learning algorithms for graph-based semi-supervised learning have been shown to produce degenerate predictions at low label rates and in imbalanced class regimes, particularly near class boundaries. We propose CutSSL: a framework for graph-based semi-supervised learning based on continuous nonconvex quadratic programming, which provably obtains \emph{integer} solutions. Our framework is naturally motivated by an \emph{exact} quadratic relaxation of a cardinality-constrained minimum-cut graph partitioning problem. Furthermore, we show our formulation is related to an optimization problem whose approximate solution is the mean-shifted Laplace learning heuristic, thus providing new insight into the performance of this heuristic. We demonstrate that CutSSL significantly surpasses the current state-of-the-art on k-nearest neighbor graphs and large real-world graph benchmarks across a variety of label rates, class imbalance, and label imbalance regimes. Our implementation is available on Colab\footnote{\url{https://colab.research.google.com/drive/1tGU5rxE1N5d0KGcNzlvZ0BgRc7_vob7b?usp=sharing}}. | Continuous Partitioning for Graph-Based Semi-Supervised Learning | [
"Chester Holtz",
"Pengwen Chen",
"Zhengchao Wan",
"Chung-Kuan Cheng",
"Gal Mishne"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hBCxxVQDBw | @inproceedings{
gonzalez2024towards,
title={Towards Scalable and Stable Parallelization of Nonlinear {RNN}s},
author={Xavier Gonzalez and Andrew Warrington and Jimmy T.H. Smith and Scott Linderman},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hBCxxVQDBw}
} | Conventional nonlinear RNNs are not naturally parallelizable across the sequence length, unlike transformers and linear RNNs. Lim et. al. therefore tackle parallelized evaluation of nonlinear RNNs, posing it as a fixed point problem solved with Newton's method. By deriving and applying a parallelized form of Newton's method, they achieve large speedups over sequential evaluation. However, their approach inherits cubic computational complexity and numerical instability. We tackle these weaknesses. To reduce the computational complexity, we apply quasi-Newton approximations and show they converge comparably, use less memory, and are faster, compared to full-Newton. To stabilize Newton's method, we leverage a connection between Newton's method damped with trust regions and Kalman smoothing. This connection allows us to stabilize the iteration, per the trust region, and use efficient parallelized Kalman algorithms to retain performance. We compare these methods empirically and highlight use cases where each algorithm excels. | Towards Scalable and Stable Parallelization of Nonlinear RNNs | [
"Xavier Gonzalez",
"Andrew Warrington",
"Jimmy T.H. Smith",
"Scott Linderman"
] | NeurIPS.cc/2024/Conference | 2407.19115 | [
"https://github.com/lindermanlab/elk"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hB5NkiET32 | @inproceedings{
zhang2024detecting,
title={Detecting Bugs with Substantial Monetary Consequences by {LLM} and Rule-based Reasoning},
author={Brian Zhang and ZHUO ZHANG},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=hB5NkiET32}
} | Financial transactions are increasingly being handled by automated programs called *smart contracts*.
However, one challenge in the adaptation of smart contracts is the presence of vulnerabilities, which can cause significant monetary loss.
In 2024, $247.88 M was lost in 20 smart contract exploits.
According to a recent study, accounting bugs (i.e., incorrect implementations of domain-specific financial models) are the most prevalent type of vulnerability,
and are one of the most difficult to find, requiring substantial human efforts.
While Large Language Models (LLMs) have shown promise in identifying these bugs, they often suffer from lack of generalization of vulnerability types, hallucinations, and problems with representing smart contracts in limited token context space.
This paper proposes a hybrid system combining LLMs and rule-based reasoning to detect accounting error vulnerabilities in smart contracts.
In particular, it utilizes the understanding capabilities of LLMs to annotate the financial meaning of variables in smart contracts, and employs rule-based reasoning to propagate the information throughout a contract's logic and to validate potential vulnerabilities.
To remedy hallucinations, we propose a feedback loop where validation is performed by providing the reasoning trace of vulnerabilities to the LLM for iterative self-reflection.
We achieve 75.6% accuracy on the labelling of financial meanings against human annotations.
Furthermore, we achieve a recall of 90.5% from running on 23 real-world smart contract projects containing 21 accounting error vulnerabilities.
Finally, we apply the automated technique on 8 recent projects, finding 4 known and 2 unknown bugs. | Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning | [
"Brian Zhang",
"ZHUO ZHANG"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=h6o6qXLmHZ | @inproceedings{
zhang2024dissect,
title={Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection},
author={Yu Zhang and Ruoyu Li and Nengwu Wu and Qing Li and Xinhan Lin and Yang Hu and Tao Li and Yong Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h6o6qXLmHZ}
} | In high-stakes sectors such as network security, IoT security, accurately distinguishing between normal and anomalous data is critical due to the significant implications for operational success and safety in decision-making. The complexity is exacerbated by the presence of unlabeled data and the opaque nature of black-box anomaly detection models, which obscure the rationale behind their predictions. In this paper, we present a novel method to interpret the decision-making processes of these models, which are essential for detecting malicious activities without labeled attack data. We put forward the Segmentation Clustering Decision Tree (SCD-Tree), designed to dissect and understand the structure of normal data distributions. The SCD-Tree integrates predictions from the anomaly detection model into its splitting criteria, enhancing the clustering process with the model's insights into anomalies. To further refine these segments, the Gaussian Boundary Delineation (GBD) algorithm is employed to define boundaries within each segmented distribution, effectively delineating normal from anomalous data points. At this point, this approach addresses the curse of dimensionality by segmenting high-dimensional data and ensures resilience to data drift and perturbations through flexible boundary fitting. We transform the intricate operations of anomaly detection into an interpretable rule's format, constructing a comprehensive set of rules for understanding. Our method's evaluation on diverse datasets and models demonstrates superior explanation accuracy, fidelity, and robustness over existing method, proving its efficacy in environments where interpretability is paramount. | Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection | [
"Yu Zhang",
"Ruoyu Li",
"Nengwu Wu",
"Qing Li",
"Xinhan Lin",
"Yang Hu",
"Tao Li",
"Yong Jiang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=h6nSE8AWCT | @inproceedings{
yoon2024tpc,
title={{TPC}: Test-time Procrustes Calibration for Diffusion-based Human Image Animation},
author={Sunjae Yoon and Gwanhyeong Koo and Younghwan Lee and Chang D. Yoo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h6nSE8AWCT}
} | Human image animation aims to generate a human motion video from the inputs of a reference human image and a target motion video. Current diffusion-based image animation systems exhibit high precision in transferring human identity into targeted motion, yet they still exhibit irregular quality in their outputs. Their optimal precision is achieved only when the physical compositions (i.e., scale and rotation) of the human shapes in the reference image and target pose frame are aligned. In the absence of such alignment, there is a noticeable decline in fidelity and consistency. Especially, in real-world environments, this compositional misalignment commonly occurs, posing significant challenges to the practical usage of current systems. To this end, we propose Test-time Procrustes Calibration (TPC), which enhances the robustness of diffusion-based image animation systems by maintaining optimal performance even when faced with compositional misalignment, effectively addressing real-world scenarios. The TPC provides a calibrated reference image for the diffusion model, enhancing its capability to understand the correspondence between human shapes in the reference and target images. Our method is simple and can be applied to any diffusion-based image animation system in a model-agnostic manner, improving the effectiveness at test time without additional training. | TPC: Test-time Procrustes Calibration for Diffusion-based Human Image Animation | [
"Sunjae Yoon",
"Gwanhyeong Koo",
"Younghwan Lee",
"Chang D. Yoo"
] | NeurIPS.cc/2024/Conference | 2410.24037 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h5zYGF68KH | @inproceedings{
kim2024pagoda,
title={PaGo{DA}: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher},
author={Dongjun Kim and Chieh-Hsin Lai and Wei-Hsiang Liao and Yuhta Takida and Naoki Murata and Toshimitsu Uesaka and Yuki Mitsufuji and Stefano Ermon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h5zYGF68KH}
} | The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffusion Autoencoder (PaGoDA), a novel pipeline that reduces the training costs through three stages: training diffusion on downsampled data, distilling the pretrained diffusion, and progressive super-resolution. With the proposed pipeline, PaGoDA achieves a $64\times$ reduced cost in training its diffusion model on $8\times$ downsampled data; while at the inference, with the single-step, it performs state-of-the-art on ImageNet across all resolutions from $64\times64$ to $512\times512$, and text-to-image. PaGoDA's pipeline can be applied directly in the latent space, adding compression alongside the pre-trained autoencoder in Latent Diffusion Models (e.g., Stable Diffusion). The code is available at https://github.com/sony/pagoda. | PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher | [
"Dongjun Kim",
"Chieh-Hsin Lai",
"Wei-Hsiang Liao",
"Yuhta Takida",
"Naoki Murata",
"Toshimitsu Uesaka",
"Yuki Mitsufuji",
"Stefano Ermon"
] | NeurIPS.cc/2024/Conference | 2405.14822 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h3k2NXu5bJ | @inproceedings{
chien2024certified,
title={Certified Machine Unlearning via Noisy Stochastic Gradient Descent},
author={Eli Chien and Haoyu Peter Wang and Ziang Chen and Pan Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h3k2NXu5bJ}
} | ``The right to be forgotten'' ensured by laws for user data privacy becomes increasingly important. Machine unlearning aims to efficiently remove the effect of certain data points on the trained model parameters so that it can be approximately the same as if one retrains the model from scratch. We propose to leverage projected noisy stochastic gradient descent for unlearning and establish its first approximate unlearning guarantee under the convexity assumption. Our approach exhibits several benefits, including provable complexity saving compared to retraining, and supporting sequential and batch unlearning. Both of these benefits are closely related to our new results on the infinite Wasserstein distance tracking of the adjacent (un)learning processes. Extensive experiments show that our approach achieves a similar utility under the same privacy constraint while using $2\%$ and $10\%$ of the gradient computations compared with the state-of-the-art gradient-based approximate unlearning methods for mini-batch and full-batch settings, respectively. | Certified Machine Unlearning via Noisy Stochastic Gradient Descent | [
"Eli Chien",
"Haoyu Peter Wang",
"Ziang Chen",
"Pan Li"
] | NeurIPS.cc/2024/Conference | 2403.17105 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h3Kv6sdTWO | @inproceedings{
song2024diffusionblend,
title={DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction},
author={Bowen Song and Jason Hu and Zhaoxu Luo and Jeffrey A Fessler and Liyue Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h3Kv6sdTWO}
} | Diffusion models face significant challenges when employed for large-scale medical image reconstruction in real practice such as 3D Computed Tomography (CT).
Due to the demanding memory, time, and data requirements, it is difficult to train a diffusion model directly on the entire volume of high-dimensional data to obtain an efficient 3D diffusion prior.
Existing works utilizing diffusion priors on single 2D image slice with hand-crafted cross-slice regularization would sacrifice the z-axis consistency, which results in severe artifacts along the z-axis.
In this work, we propose a novel framework that enables learning the 3D image prior through position-aware 3D-patch diffusion score blending for reconstructing large-scale 3D medical images. To the best of our knowledge, we are the first to utilize a 3D-patch diffusion prior for 3D medical image reconstruction.
Extensive experiments on sparse view and limited angle CT reconstruction
show that our DiffusionBlend method significantly outperforms previous methods
and achieves state-of-the-art performance on real-world CT reconstruction problems with high-dimensional 3D image (i.e., $256 \times 256 \times 500$). Our algorithm also comes with better or comparable computational efficiency than previous state-of-the-art methods. Code is available at https://github.com/efzero/DiffusionBlend. | DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction | [
"Bowen Song",
"Jason Hu",
"Zhaoxu Luo",
"Jeffrey A Fessler",
"Liyue Shen"
] | NeurIPS.cc/2024/Conference | 2406.10211 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h3BdT2UMWQ | @inproceedings{
xie2024breaking,
title={Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model},
author={Wenjia Xie and Hao Wang and Luankang Zhang and Rui Zhou and Defu Lian and Enhong Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h3BdT2UMWQ}
} | Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior sequences. We revisit SR from a novel information-theoretic perspective and find that conventional sequential modeling methods fail to adequately capture the randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the DDSR model, which uses fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests. Formally based on diffusion transition processes in discrete state spaces, which is unlike common diffusion models such as DDPM that operate in continuous domains. It is better suited for discrete data, using structured transitions instead of arbitrary noise introduction to avoid information loss. Additionally, to address the inefficiency of matrix transformations due to the vast discrete space, we use semantic labels derived from quantization or RQ-VAE to replace item IDs, enhancing efficiency and improving cold start issues. Testing on three public benchmark datasets shows that DDSR outperforms existing state-of-the-art methods in various settings, demonstrating its potential and effectiveness in handling SR tasks. | Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model | [
"Wenjia Xie",
"Hao Wang",
"Luankang Zhang",
"Rui Zhou",
"Defu Lian",
"Enhong Chen"
] | NeurIPS.cc/2024/Conference | 2410.23994 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h34jVnPo1c | @inproceedings{
chen2024doubly,
title={Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation},
author={Yunlu Chen and Francisco Vicente Carrasco and Christian H{\"a}ne and Giljoo Nam and Jean-Charles Bazin and Fernando De la Torre},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h34jVnPo1c}
} | We introduce a doubly hierarchical generative representation for strand-based hair geometry that progresses from coarse, low-pass filtered guide hair to densely populated hair strands rich in high-frequency details. We employ the Discrete Cosine Transform (DCT) to separate low-frequency structural curves from high-frequency curliness and noise, avoiding the Gibbs' oscillation issues associated with the standard Fourier transform in open curves. Unlike the guide hair sampled from the scalp UV map grids which may lose capturing details of the hairstyle in existing methods, our method samples optimal sparse guide strands by utilizing $k$-medoids clustering centres from low-pass filtered dense strands, which more accurately retain the hairstyle's inherent characteristics. The proposed variational autoencoder-based generation network, with an architecture inspired by geometric deep learning and implicit neural representations, facilitates flexible, off-the-grid guide strand modelling and enables the completion of dense strands in any quantity and density, drawing on principles from implicit neural representations. Empirical evaluations confirm the capacity of the model to generate convincing guide hair and dense strands, complete with nuanced high-frequency details. | Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation | [
"Yunlu Chen",
"Francisco Vicente Carrasco",
"Christian Häne",
"Giljoo Nam",
"Jean-Charles Bazin",
"Fernando De la Torre"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=h2e4G2YiwR | @inproceedings{
lin2024action,
title={Action Imitation in Common Action Space for Customized Action Image Synthesis},
author={Wang Lin and Jingyuan Chen and Jiaxin Shi and Zirun Guo and Yichen Zhu and Zehan Wang and Tao Jin and Zhou Zhao and Fei Wu and Shuicheng YAN and Hanwang Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h2e4G2YiwR}
} | We propose a novel method, \textbf{TwinAct}, to tackle the challenge of decoupling actions and actors in order to customize the text-guided diffusion models (TGDMs) for few-shot action image generation. TwinAct addresses the limitations of existing methods that struggle to decouple actions from other semantics (e.g., the actor's appearance) due to the lack of an effective inductive bias with few exemplar images. Our approach introduces a common action space, which is a textual embedding space focused solely on actions, enabling precise customization without actor-related details. Specifically, TwinAct involves three key steps: 1) Building common action space based on a set of representative action phrases; 2) Imitating the customized action within the action space; and 3) Generating highly adaptable customized action images in diverse contexts with action similarity loss. To comprehensively evaluate TwinAct, we construct a novel benchmark, which provides sample images with various forms of actions. Extensive experiments demonstrate TwinAct's superiority in generating accurate, context-independent customized actions while maintaining the identity consistency of different subjects, including animals, humans, and even customized actors. | Action Imitation in Common Action Space for Customized Action Image Synthesis | [
"Wang Lin",
"Jingyuan Chen",
"Jiaxin Shi",
"Zirun Guo",
"Yichen Zhu",
"Zehan Wang",
"Tao Jin",
"Zhou Zhao",
"Fei Wu",
"Shuicheng YAN",
"Hanwang Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=h1iMVi2iEM | @inproceedings{
sun2024afedpd,
title={A-Fed{PD}: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs},
author={Yan Sun and Li Shen and Dacheng Tao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h1iMVi2iEM}
} | As a popular paradigm for juggling data privacy and collaborative training, federated learning (FL) is flourishing to distributively process the large scale of heterogeneous datasets on edged clients. Due to bandwidth limitations and security considerations, it ingeniously splits the original problem into multiple subproblems to be solved in parallel, which empowers primal dual solutions to great application values in FL. In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, which we say is a ``dual drift'' caused by dual hysteresis of those longstanding inactive clients under partial participation training. To further address this problem, we propose a novel Aligned Federated Primal Dual (A-FedPD) method, which constructs virtual dual updates to align global consensus and local dual variables for those protracted unparticipated local clients. Meanwhile, we provide a comprehensive analysis of the optimization and generalization efficiency for the A-FedPD method on smooth non-convex objectives, which confirms its high efficiency and practicality. Extensive experiments are conducted on several classical FL setups to validate the effectiveness of our proposed method. | A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs | [
"Yan Sun",
"Li Shen",
"Dacheng Tao"
] | NeurIPS.cc/2024/Conference | 2409.18915 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=h1grUs6CjN | @inproceedings{
tsilivis2024the,
title={The Price of Implicit Bias in Adversarially Robust Generalization},
author={Nikolaos Tsilivis and Natalie Frank and Nathan Srebro and Julia Kempe},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h1grUs6CjN}
} | We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization. In classification settings under adversarial perturbations with linear models, we study what type of regularization should ideally be applied for a given perturbation set to improve (robust) generalization. We then show that the implicit bias of optimization in robust ERM can significantly affect the robustness of the model and identify two ways this can happen; either through the optimization algorithm or the architecture. We verify our predictions in simulations with synthetic data and experimentally study the importance of implicit bias in robust ERM with deep neural networks. | The Price of Implicit Bias in Adversarially Robust Generalization | [
"Nikolaos Tsilivis",
"Natalie Frank",
"Nathan Srebro",
"Julia Kempe"
] | NeurIPS.cc/2024/Conference | 2406.04981 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h15RyEj151 | @inproceedings{
ran-milo2024provable,
title={Provable Benefits of Complex Parameterizations for Structured State Space Models},
author={Yuval Ran-Milo and Eden Lumbroso and Edo Cohen-Karlik and Raja Giryes and Amir Globerson and Nadav Cohen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h15RyEj151}
} | Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. Theoretically explaining the benefits of complex parameterizations for SSMs is an open problem. The current paper takes a step towards its resolution, by establishing formal gaps between real and complex diagonal SSMs. Firstly, we prove that while a moderate dimension suffices in order for a complex SSM to express all mappings of a real SSM, a much higher dimension is needed for a real SSM to express mappings of a complex SSM. Secondly, we prove that even if the dimension of a real SSM is high enough to express a given mapping, typically, doing so requires the parameters of the real SSM to hold exponentially large values, which cannot be learned in practice. In contrast, a complex SSM can express any given mapping with moderate parameter values. Experiments corroborate our theory, and suggest a potential extension of the theory that accounts for selectivity, a new architectural feature yielding state of the art performance. | Provable Benefits of Complex Parameterizations for Structured State Space Models | [
"Yuval Ran-Milo",
"Eden Lumbroso",
"Edo Cohen-Karlik",
"Raja Giryes",
"Amir Globerson",
"Nadav Cohen"
] | NeurIPS.cc/2024/Conference | 2410.14067 | [
"https://github.com/edenlum/ssmcomplexparambenefits"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h0rbjHyWoa | @inproceedings{
gao2024generalize,
title={Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts},
author={Zhitong Gao and Bingnan Li and Mathieu Salzmann and Xuming He},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h0rbjHyWoa}
} | In open-world scenarios, where both novel classes and domains may exist, an ideal segmentation model should detect anomaly classes for safety and generalize to new domains. However, existing methods often struggle to distinguish between domain-level and semantic-level distribution shifts, leading to poor OOD detection or domain generalization performance. In this work, we aim to equip the model to generalize effectively to covariate-shift regions while precisely identifying semantic-shift regions. To achieve this, we design a novel generative augmentation method to produce coherent images that incorporate both anomaly (or novel) objects and various covariate shifts at both image and object levels. Furthermore, we introduce a training strategy that recalibrates uncertainty specifically for semantic shifts and enhances the feature extractor to align features associated with domain shifts. We validate the effectiveness of our method across benchmarks featuring both semantic and domain shifts. Our method achieves state-of-the-art performance across all benchmarks for both OOD detection and domain generalization. Code is available at https://github.com/gaozhitong/MultiShiftSeg. | Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts | [
"Zhitong Gao",
"Bingnan Li",
"Mathieu Salzmann",
"Xuming He"
] | NeurIPS.cc/2024/Conference | 2411.03829 | [
"https://github.com/gaozhitong/multishiftseg"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=h0a3p5WtXU | @inproceedings{
islamov2024loss,
title={Loss Landscape Characterization of Neural Networks without Over-Parametrization},
author={Rustem Islamov and Niccol{\`o} Ajroldi and Antonio Orvieto and Aurelien Lucchi},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h0a3p5WtXU}
} | Modern machine learning heavily depends on the effectiveness of optimization techniques. While deep learning models have achieved remarkable empirical results in training, their theoretical underpinnings remain somewhat elusive. Ensuring the convergence of optimization methods requires imposing specific structures on the objective function which often do not hold in practice. One prominent example is the widely recognized Polyak-Lojasiewicz (PL) inequality, which has garnered considerable attention in recent years. However, validating such assumptions for deep neural networks entails substantial and often impractical levels of over-parametrization. In order to address this limitation, we propose a novel class of functions that can characterize the loss landscape of modern deep models without requiring extensive over-parametrization and can also include saddle points. Crucially, we prove that gradient-based optimizers possess theoretical guarantees of convergence under this assumption. Finally, we validate the soundness of our assumption through both theoretical analysis and empirical experimentation across a diverse range of deep learning models. | Loss Landscape Characterization of Neural Networks without Over-Parametrization | [
"Rustem Islamov",
"Niccolò Ajroldi",
"Antonio Orvieto",
"Aurelien Lucchi"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=h024LpF3bZ | @inproceedings{
chen2024weakevalstrong,
title={Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of {LLM}s with Situation Puzzles},
author={Qi Chen and Bowen Zhang and Gang Wang and Qi Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=h024LpF3bZ}
} | While advancements in NLP have significantly improved the performance of Large Language Models (LLMs) on tasks requiring vertical thinking, their lateral thinking capabilities remain under-explored and challenging to measure due to the complexity of assessing creative thought processes and the scarcity of relevant data. To address these challenges, we introduce SPLAT, a benchmark leveraging Situation Puzzles to evaluate and elicit LAteral Thinking of LLMs. This benchmark, containing 975 graded situation puzzles across three difficulty levels, employs a new multi-turn player-judge framework instead of the traditional model-based evaluation, which often necessitates a stronger evaluation model. This framework simulates an interactive game where the model (player) asks the evaluation model (judge) questions about an incomplete story to infer the full scenario. The judge answers based on a detailed reference scenario or evaluates if the player's predictions align with the reference one. This approach lessens dependence on more robust evaluation models, enabling the assessment of state-of-the-art LLMs. The experiments demonstrate that a robust evaluation model, such as WizardLM-2, closely matches human judgements in both intermediate question-answering and final scenario accuracy, achieving over 80% agreement--similar to the agreement levels among humans. Furthermore, applying data and reasoning processes from our benchmark to other lateral thinking-related benchmarks, e.g., RiddleSense and BrainTeaser, leads to performance enhancements. This suggests that our benchmark effectively evaluates and elicits the lateral thinking abilities of LLMs. | Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles | [
"Qi Chen",
"Bowen Zhang",
"Gang Wang",
"Qi Wu"
] | NeurIPS.cc/2024/Conference | 2410.06733 | [
"https://github.com/chenqi008/LateralThinking"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gzh9nTUtsY | @inproceedings{
li2024least,
title={Least Squares Regression Can Exhibit Under-Parameterized Double Descent},
author={Xinyue Li and Rishi Sonthalia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gzh9nTUtsY}
} | The relationship between the number of training data points, the number of parameters, and the generalization capabilities of models has been widely studied. Previous work has shown that double descent can occur in the over-parameterized regime and that the standard bias-variance trade-off holds in the under-parameterized regime. These works provide multiple reasons for the existence of the peak. We postulate that the location of the peak depends on the technical properties of both the spectrum as well as the eigenvectors of the sample covariance. We present two simple examples that provably exhibit double descent in the under-parameterized regime and do not seem to occur for reasons provided in prior work. | Least Squares Regression Can Exhibit Under-Parameterized Double Descent | [
"Xinyue Li",
"Rishi Sonthalia"
] | NeurIPS.cc/2024/Conference | 2305.14689 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gzQARCgIsI | @inproceedings{
dhawan2024endtoend,
title={End-To-End Causal Effect Estimation from Unstructured Natural Language Data},
author={Nikita Dhawan and Leonardo Cotta and Karen Ullrich and Rahul Krishnan and Chris J. Maddison},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gzQARCgIsI}
} | Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions. This increases both the cost and time-to-completion for studies. We show how large, diverse observational text data can be mined with large language models (LLMs) to produce inexpensive causal effect estimates under appropriate causal assumptions. We introduce _NATURAL_, a novel family of causal effect estimators built with LLMs that operate over datasets of unstructured text. Our estimators use LLM conditional distributions (over variables of interest, given the text data) to assist in the computation of classical estimators of causal effect. We overcome a number of technical challenges to realize this idea, such as automating data curation and using LLMs to impute missing information. We prepare six (two synthetic and four real) observational datasets, paired with corresponding ground truth in the form of randomized trials, which we used to systematically evaluate each step of our pipeline. NATURAL estimators demonstrate remarkable performance, yielding causal effect estimates that fall within 3 percentage points of their ground truth counterparts, including on real-world Phase 3/4 clinical trials. Our results suggest that unstructured text data is a rich source of causal effect information, and NATURAL is a first step towards an automated pipeline to tap this resource. | End-To-End Causal Effect Estimation from Unstructured Natural Language Data | [
"Nikita Dhawan",
"Leonardo Cotta",
"Karen Ullrich",
"Rahul Krishnan",
"Chris J. Maddison"
] | NeurIPS.cc/2024/Conference | 2407.07018 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gxMfNArldP | @inproceedings{
wang2024qvlm,
title={Q-{VLM}: Post-training Quantization for Large Vision-Language Models},
author={Changyuan Wang and Ziwei Wang and Xiuwei Xu and Yansong Tang 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=gxMfNArldP}
} | In this paper, we propose a post-training quantization framework of large vision-language models (LVLMs) for efficient multi-modal inference. Conventional quantization methods sequentially search the layer-wise rounding functions by minimizing activation discretization errors, which fails to acquire optimal quantization strategy without considering cross-layer dependency. On the contrary, we mine the cross-layer dependency that significantly influences discretization errors of the entire vision-language model, and embed this dependency into optimal quantization strategy searching with low search cost. Specifically, we observe the strong correlation between the activation entropy and the cross-layer dependency concerning output discretization errors. Therefore, we employ the entropy as the proxy to partition blocks optimally, which aims to achieve satisfying trade-offs between discretization errors and the search cost. Moreover, we optimize the visual encoder to disentangle the cross-layer dependency for fine-grained decomposition of search space, so that the search cost is further reduced without harming the quantization accuracy. Experimental results demonstrate that our method compresses the memory by 2.78x and increase generate speed by 1.44x about 13B LLaVA model without performance degradation on diverse multi-modal reasoning tasks. | Q-VLM: Post-training Quantization for Large Vision-Language Models | [
"Changyuan Wang",
"Ziwei Wang",
"Xiuwei Xu",
"Yansong Tang",
"Jie Zhou",
"Jiwen Lu"
] | NeurIPS.cc/2024/Conference | 2410.08119 | [
"https://github.com/changyuanwang17/qvlm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gwd3MQufGP | @inproceedings{
yang2024kptllm,
title={Kpt{LLM}: Unveiling the Power of Large Language Model for Keypoint Comprehension},
author={Jie Yang and Wang ZENG and Sheng Jin and Lumin Xu and Wentao Liu and Chen Qian and Ruimao Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gwd3MQufGP}
} | Recent advancements in Multimodal Large Language Models (MLLMs) have greatly improved their abilities in image understanding. However, these models often struggle with grasping pixel-level semantic details, e.g., the keypoints of an object. To bridge this gap, we introduce the novel challenge of Semantic Keypoint Comprehension, which aims to comprehend keypoints across different task scenarios, including keypoint semantic understanding, visual prompt-based keypoint detection, and textual prompt-based keypoint detection. Moreover, we introduce KptLLM, a unified multimodal model that utilizes an identify-then-detect strategy to effectively address these challenges. KptLLM underscores the initial discernment of semantics in keypoints, followed by the precise determination of their positions through a chain-of-thought process. With several carefully designed modules, KptLLM adeptly handles various modality inputs, facilitating the interpretation of both semantic contents and keypoint locations. Our extensive experiments demonstrate KptLLM's superiority in various keypoint detection benchmarks and its unique semantic capabilities in interpreting keypoints. | KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension | [
"Jie Yang",
"Wang ZENG",
"Sheng Jin",
"Lumin Xu",
"Wentao Liu",
"Chen Qian",
"Ruimao Zhang"
] | NeurIPS.cc/2024/Conference | 2411.01846 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gvtCR7dHJ3 | @inproceedings{
hwang2024dual,
title={Dual Cone Gradient Descent for Training Physics-Informed Neural Networks},
author={Youngsik Hwang and Dongyoung Lim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gvtCR7dHJ3}
} | Physics-informed neural networks (PINNs) have emerged as a prominent approach for solving partial differential equations (PDEs) by minimizing a combined loss function that incorporates both boundary loss and PDE residual loss. Despite their remarkable empirical performance in various scientific computing tasks, PINNs often fail to generate reasonable solutions, and such pathological behaviors remain difficult to explain and resolve. In this paper, we identify that PINNs can be adversely trained when gradients of each loss function exhibit a significant imbalance in their magnitudes and present a negative inner product value. To address these issues, we propose a novel optimization framework, *Dual Cone Gradient Descent* (DCGD), which adjusts the direction of the updated gradient to ensure it falls within a dual cone region. This region is defined as a set of vectors where the inner products with both the gradients of the PDE residual loss and the boundary loss are non-negative. Theoretically, we analyze the convergence properties of DCGD algorithms in a non-convex setting. On a variety of benchmark equations, we demonstrate that DCGD outperforms other optimization algorithms in terms of various evaluation metrics. In particular, DCGD achieves superior predictive accuracy and enhances the stability of training for failure modes of PINNs and complex PDEs, compared to existing optimally tuned models. Moreover, DCGD can be further improved by combining it with popular strategies for PINNs, including learning rate annealing and the Neural Tangent Kernel (NTK). | Dual Cone Gradient Descent for Training Physics-Informed Neural Networks | [
"Youngsik Hwang",
"Dongyoung Lim"
] | NeurIPS.cc/2024/Conference | 2409.18426 | [
"https://github.com/youngsikhwang/Dual-Cone-Gradient-Descent"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gvlOQC6oP1 | @inproceedings{
wang2024image,
title={Image Copy Detection for Diffusion Models},
author={Wenhao Wang and Yifan Sun and Zhentao Tan and Yi Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gvlOQC6oP1}
} | Images produced by diffusion models are increasingly popular in digital artwork and visual marketing. However, such generated images might replicate content from existing ones and pose the challenge of content originality. Existing Image Copy Detection (ICD) models, though accurate in detecting hand-crafted replicas, overlook the challenge from diffusion models. This motivates us to introduce ICDiff, the first ICD specialized for diffusion models. To this end, we construct a Diffusion-Replication (D-Rep) dataset and correspondingly propose a novel deep embedding method. D-Rep uses a state-of-the-art diffusion model (Stable Diffusion V1.5) to generate 40, 000 image-replica pairs, which are manually annotated into 6 replication levels ranging from 0 (no replication) to 5 (total replication). Our method, PDF-Embedding, transforms the replication level of each image-replica pair into a probability density function (PDF) as the supervision signal. The intuition is that the probability of neighboring replication levels should be continuous and smooth. Experimental results show that PDF-Embedding surpasses protocol-driven methods and non-PDF choices on the D-Rep test set. Moreover, by utilizing PDF-Embedding, we find that the replication ratios of well-known diffusion models against an open-source gallery range from 10% to 20%. The project is publicly available at https://icdiff.github.io/. | Image Copy Detection for Diffusion Models | [
"Wenhao Wang",
"Yifan Sun",
"Zhentao Tan",
"Yi Yang"
] | NeurIPS.cc/2024/Conference | 2409.19952 | [
""
] | https://huggingface.co/papers/2409.19952 | 1 | 12 | 3 | 4 | [] | [
"WenhaoWang/D-Rep"
] | [] | [] | [
"WenhaoWang/D-Rep"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=gvg8pExqdd | @inproceedings{
kim2024diversify,
title={Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec},
author={Jun-Hyuk Kim and Seungeon Kim and Won-Hee Lee and Dokwan Oh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gvg8pExqdd}
} | Designing a fast and effective entropy model is challenging but essential for practical application of neural codecs. Beyond spatial autoregressive entropy models, more efficient backward adaptation-based entropy models have been recently developed. They not only reduce decoding time by using smaller number of modeling steps but also maintain or even improve rate--distortion performance by leveraging more diverse contexts for backward adaptation. Despite their significant progress, we argue that their performance has been limited by the simple adoption of the design convention for forward adaptation: using only a single type of hyper latent representation, which does not provide sufficient contextual information, especially in the first modeling step. In this paper, we propose a simple yet effective entropy modeling framework that leverages sufficient contexts for forward adaptation without compromising on bit-rate. Specifically, we introduce a strategy of diversifying hyper latent representations for forward adaptation, i.e., using two additional types of contexts along with the existing single type of context. In addition, we present a method to effectively use the diverse contexts for contextualizing the current elements to be encoded/decoded. By addressing the limitation of the previous approach, our proposed framework leads to significant performance improvements. Experimental results on popular datasets show that our proposed framework consistently improves rate-distortion performance across various bit-rate regions, e.g., 3.73\% BD-rate gain over the state-of-the-art baseline on the Kodak dataset. | Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec | [
"Jun-Hyuk Kim",
"Seungeon Kim",
"Won-Hee Lee",
"Dokwan Oh"
] | NeurIPS.cc/2024/Conference | 2411.05832 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=guzWIg7ody | @inproceedings{
zhang2024nonparametric,
title={Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks},
author={Zixuan Zhang and Kaiqi Zhang and Minshuo Chen and Yuma Takeda and Mengdi Wang and Tuo Zhao and Yu-Xiang Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=guzWIg7ody}
} | Convolutional residual neural networks (ConvResNets), though overparametersized, can achieve remarkable prediction performance in practice, which cannot be well explained by conventional wisdom. To bridge this gap, we study the performance of ConvResNeXts trained with weight decay, which cover ConvResNets as a special case, from the perspective of nonparametric classification. Our analysis allows for infinitely many building blocks in ConvResNeXts, and shows that weight decay implicitly enforces sparsity on these blocks. Specifically, we consider a smooth target function supported on a low-dimensional manifold, then prove that ConvResNeXts can adapt to the function smoothness and low-dimensional structures and efficiently learn the function without suffering from the curse of dimensionality. Our findings partially justify the advantage of overparameterized ConvResNeXts over conventional machine learning models. | Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks | [
"Zixuan Zhang",
"Kaiqi Zhang",
"Minshuo Chen",
"Yuma Takeda",
"Mengdi Wang",
"Tuo Zhao",
"Yu-Xiang Wang"
] | NeurIPS.cc/2024/Conference | 2307.01649 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gtU2eLSAmO | @inproceedings{
dong2024brainjepa,
title={Brain-{JEPA}: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking},
author={Zijian Dong and Li Ruilin and Yilei Wu and Thuan Tinh Nguyen and Joanna Su Xian Chong and Fang Ji and Nathanael Ren Jie Tong and Christopher Li Hsian Chen and Juan Helen Zhou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gtU2eLSAmO}
} | We introduce *Brain-JEPA*, a brain dynamics foundation model with the Joint-Embedding Predictive Architecture (JEPA). This pioneering model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait prediction through fine-tuning. Furthermore, it excels in off-the-shelf evaluations (e.g., linear probing) and demonstrates superior generalizability across different ethnic groups, surpassing the previous large model for brain activity significantly. Brain-JEPA incorporates two innovative techniques: **Brain Gradient Positioning** and **Spatiotemporal Masking**. Brain Gradient Positioning introduces a functional coordinate system for brain functional parcellation, enhancing the positional encoding of different Regions of Interest (ROIs). Spatiotemporal Masking, tailored to the unique characteristics of fMRI data, addresses the challenge of heterogeneous time-series patches. These methodologies enhance model performance and advance our understanding of the neural circuits underlying cognition. Overall, Brain-JEPA is paving the way to address pivotal questions of building brain functional coordinate system and masking brain activity at the AI-neuroscience interface, and setting a potentially new paradigm in brain activity analysis through downstream adaptation. | Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking | [
"Zijian Dong",
"Li Ruilin",
"Yilei Wu",
"Thuan Tinh Nguyen",
"Joanna Su Xian Chong",
"Fang Ji",
"Nathanael Ren Jie Tong",
"Christopher Li Hsian Chen",
"Juan Helen Zhou"
] | NeurIPS.cc/2024/Conference | 2409.19407 | [
"https://github.com/Eric-LRL/Brain-JEPA"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=grrefkWEES | @inproceedings{
liang2024diffusiond,
title={Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models},
author={HANWEN LIANG and Yuyang Yin and Dejia Xu and hanxue liang and Zhangyang Wang and Konstantinos N Plataniotis and Yao Zhao and Yunchao Wei},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=grrefkWEES}
} | The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rely on multiple images or video diffusion models, utilizing score distillation sampling for optimization or generating pseudo novel views for direct supervision. However, these methods are hindered by slow optimization speeds and multi-view inconsistency issues. Spatial and temporal consistency in 4D geometry has been extensively explored respectively in 3D-aware diffusion models and traditional monocular video diffusion models. Building on this foundation, we propose a strategy to migrate the temporal consistency in video diffusion models to the spatial-temporal consistency required for 4D generation. Specifically, we present a novel framework, \textbf{Diffusion4D}, for efficient and scalable 4D content generation. Leveraging a meticulously curated dynamic 3D dataset, we develop a 4D-aware video diffusion model capable of synthesizing orbital views of dynamic 3D assets. To control the dynamic strength of these assets, we introduce a 3D-to-4D motion magnitude metric as guidance. Additionally, we propose a novel motion magnitude reconstruction loss and 3D-aware classifier-free guidance to refine the learning and generation of motion dynamics. After obtaining orbital views of the 4D asset, we perform explicit 4D construction with Gaussian splatting in a coarse-to-fine manner. Extensive experiments demonstrate that our method surpasses prior state-of-the-art techniques in terms of generation efficiency and 4D geometry consistency across various prompt modalities. | Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models | [
"HANWEN LIANG",
"Yuyang Yin",
"Dejia Xu",
"hanxue liang",
"Zhangyang Wang",
"Konstantinos N Plataniotis",
"Yao Zhao",
"Yunchao Wei"
] | NeurIPS.cc/2024/Conference | 2405.16645 | [
""
] | https://huggingface.co/papers/2405.16645 | 1 | 0 | 1 | 8 | [] | [
"hw-liang/Diffusion4D"
] | [] | [] | [
"hw-liang/Diffusion4D"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=gojL67CfS8 | @inproceedings{
tian2024visual,
title={Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction},
author={Keyu Tian and Yi Jiang and Zehuan Yuan and BINGYUE PENG and Liwei Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gojL67CfS8}
} | We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction". This simple, intuitive methodology allows autoregressive (AR) transformers to learn visual distributions fast and generalize well: VAR, for the first time, makes GPT-style AR models surpass diffusion transformers in image generation. On ImageNet 256x256 benchmark, VAR significantly improve AR baseline by improving Frechet inception distance (FID) from 18.65 to 1.73, inception score (IS) from 80.4 to 350.2, with around 20x faster inference speed. It is also empirically verified that VAR outperforms the Diffusion Transformer (DiT) in multiple dimensions including image quality, inference speed, data efficiency, and scalability. Scaling up VAR models exhibits clear power-law scaling laws similar to those observed in LLMs, with linear correlation coefficients near -0.998 as solid evidence. VAR further showcases zero-shot generalization ability in downstream tasks including image in-painting, out-painting, and editing. These results suggest VAR has initially emulated the two important properties of LLMs: Scaling Laws and zero-shot task generalization. We have released all models and codes to promote the exploration of AR/VAR models for visual generation and unified learning. | Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction | [
"Keyu Tian",
"Yi Jiang",
"Zehuan Yuan",
"BINGYUE PENG",
"Liwei Wang"
] | NeurIPS.cc/2024/Conference | 2404.02905 | [
"https://github.com/FoundationVision/VAR"
] | https://huggingface.co/papers/2404.02905 | 4 | 64 | 3 | 5 | [
"FoundationVision/var"
] | [] | [] | [
"FoundationVision/var"
] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=go4zzXBWVs | @inproceedings{
zanella2024boosting,
title={Boosting Vision-Language Models with Transduction},
author={Maxime Zanella and Beno{\^\i}t G{\'e}rin and Ismail Ben Ayed},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=go4zzXBWVs}
} | Transduction is a powerful paradigm that leverages the structure of unlabeled data to boost predictive accuracy. We present TransCLIP, a novel and computationally efficient transductive approach designed for Vision-Language Models (VLMs). TransCLIP is applicable as a plug-and-play module on top of popular inductive zero- and few-shot models, consistently improving their performances. Our new objective function can be viewed as a regularized maximum-likelihood estimation, constrained by a KL divergence penalty that integrates the text-encoder knowledge and guides the transductive learning process. We further derive an iterative Block Majorize-Minimize (BMM) procedure for optimizing our objective, with guaranteed convergence and decoupled sample-assignment updates, yielding computationally efficient transduction for large-scale datasets. We report comprehensive evaluations, comparisons, and ablation studies that demonstrate: (i) Transduction can greatly enhance the generalization capabilities of inductive pretrained zero- and few-shot VLMs; (ii) TransCLIP substantially outperforms standard transductive few-shot learning methods relying solely on vision features, notably due to the KL-based language constraint. | Boosting Vision-Language Models with Transduction | [
"Maxime Zanella",
"Benoît Gérin",
"Ismail Ben Ayed"
] | NeurIPS.cc/2024/Conference | 2406.01837 | [
"https://github.com/MaxZanella/transduction-for-vlms"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=gnnmB7y0Xx | @inproceedings{
li2024incontext,
title={In-Context Learning State Vector with Inner and Momentum Optimization},
author={Dongfang Li and zhenyu liu and Xinshuo Hu and Zetian Sun and Baotian Hu and Min Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gnnmB7y0Xx}
} | Large Language Models (LLMs) have exhibited an impressive ability to perform In-Context Learning (ICL) from only a few examples. Recent works have indicated that the functions learned by ICL can be represented through compressed vectors derived from the transformer. However, the working mechanisms and optimization of these vectors are yet to be thoroughly explored. In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introducing the concept of state vector. Inspired by the works on model soup and momentum-based gradient descent, we propose inner and momentum optimization methods that are applied to refine the state vector progressively as test-time adaptation. Moreover, we simulate state vector aggregation in the multiple example setting, where demonstrations comprising numerous examples are usually too lengthy for regular ICL, and further propose a divide-and-conquer aggregation method to address this challenge. We conduct extensive experiments using Llama-2 and GPT-J in both zero-shot setting and few-shot setting. The experimental results show that our optimization method effectively enhances the state vector and achieves the state-of-the-art performance on diverse tasks. | In-Context Learning State Vector with Inner and Momentum Optimization | [
"Dongfang Li",
"zhenyu liu",
"Xinshuo Hu",
"Zetian Sun",
"Baotian Hu",
"Min Zhang"
] | NeurIPS.cc/2024/Conference | 2404.11225 | [
"https://github.com/hitsz-tmg/icl-state-vector"
] | https://huggingface.co/papers/2404.11225 | 1 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=gnXTDQyxlU | @inproceedings{
zhang2024pivotr,
title={{PIVOT}-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation},
author={Kaidong Zhang and Pengzhen Ren and Bingqian Lin and Junfan Lin and Shikui Ma and Hang Xu and Xiaodan Liang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gnXTDQyxlU}
} | Language-guided robotic manipulation is a challenging task that requires an embodied agent to follow abstract user instructions to accomplish various complex manipulation tasks. Previous work generally maps instructions and visual perceptions directly to low-level executable actions, neglecting the modeling of critical waypoints (e.g., key states of “close to/grab/move up” in action trajectories) in manipulation tasks.
To address this issue, we propose a PImitive-driVen waypOinT-aware world model for Robotic manipulation (PIVOT-R) that focuses solely on the prediction of task-relevant waypoints. Specifically, PIVOT-R consists of a Waypoint-aware World Model (WAWM) and a lightweight action prediction module. The former performs primitive action parsing and primitive-driven waypoint prediction, while the latter focuses on decoding low-level actions. Additionally, we also design an asynchronous hierarchical executor (AHE) for PIVOT-R, which can use different execution frequencies for different modules of the model, thereby helping the model reduce computational redundancy and improve model execution efficiency. Our PIVOT-R outperforms state-of-the-art (SoTA) open-source models on the SeaWave benchmark, achieving an average relative improvement of 19.45% across four levels of instruction tasks. Moreover, compared to the synchronously executed PIVOT-R, the execution efficiency of PIVOT-R with AHE is increased by 28-fold, with only a 2.9% drop in performance. These results provide compelling evidence that our PIVOT-R can significantly improve both the performance and efficiency of robotic manipulation. | PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation | [
"Kaidong Zhang",
"Pengzhen Ren",
"Bingqian Lin",
"Junfan Lin",
"Shikui Ma",
"Hang Xu",
"Xiaodan Liang"
] | NeurIPS.cc/2024/Conference | 2410.10394 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gmf5Aj01Hz | @inproceedings{
dai2024sarad,
title={{SARAD}: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series},
author={Zhihao Dai and Ligang He and Shuanghua Yang and Matthew Leeke},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gmf5Aj01Hz}
} | Anomaly detection in time series data is fundamental to the design, deployment, and evaluation of industrial control systems. Temporal modeling has been the natural focus of anomaly detection approaches for time series data. However, the focus on temporal modeling can obscure or dilute the spatial information that can be used to capture complex interactions in multivariate time series. In this paper, we propose SARAD, an approach that leverages spatial information beyond data autoencoding errors to improve the detection and diagnosis of anomalies. SARAD trains a Transformer to learn the spatial associations, the pairwise inter-feature relationships which ubiquitously characterize such feedback-controlled systems. As new associations form and old ones dissolve, SARAD applies subseries division to capture their changes over time. Anomalies exhibit association descending patterns, a key phenomenon we exclusively observe and attribute to the disruptive nature of anomalies detaching anomalous features from others. To exploit the phenomenon and yet dismiss non-anomalous descent, SARAD performs anomaly detection via autoencoding in the association space. We present experimental results to demonstrate that SARAD achieves state-of-the-art performance, providing robust anomaly detection and a nuanced understanding of anomalous events. | SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series | [
"Zhihao Dai",
"Ligang He",
"Shuanghua Yang",
"Matthew Leeke"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=glgZZAfssH | @inproceedings{
limbeck2024metric,
title={Metric Space Magnitude for Evaluating the Diversity of Latent Representations},
author={Katharina Limbeck and Rayna Andreeva and Rik Sarkar and Bastian Rieck},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=glgZZAfssH}
} | The *magnitude* of a metric space is a novel
invariant that provides a measure of the 'effective size' of a space across
multiple scales, while also capturing numerous geometrical properties, such as curvature, density, or entropy.
We develop a family of magnitude-based measures of the intrinsic
diversity of latent representations, formalising a novel notion of
dissimilarity between magnitude functions of finite metric spaces.
Our measures are provably stable under perturbations of the data, can be
efficiently calculated, and enable a rigorous multi-scale characterisation and comparison of
latent representations.
We show their utility and superior performance across different domains and tasks, including
the automated estimation of diversity,
the detection of mode collapse, and
the evaluation of generative models for text, image, and graph data. | Metric Space Magnitude for Evaluating the Diversity of Latent Representations | [
"Katharina Limbeck",
"Rayna Andreeva",
"Rik Sarkar",
"Bastian Rieck"
] | NeurIPS.cc/2024/Conference | 2311.16054 | [
"https://github.com/aidos-lab/magnipy"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=glfYOAzh2f | @inproceedings{
lee2024selective,
title={Selective Generation for Controllable Language Models},
author={Minjae Lee and Kyungmin Kim and Taesoo Kim and Sangdon Park},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=glfYOAzh2f}
} | Trustworthiness of generative language models (GLMs) is crucial in their deployment to critical decision making systems. Hence, certified risk control methods such as selective prediction and conformal prediction have been applied to mitigating the hallucination problem in various supervised downstream tasks. However, the lack of appropriate correctness metric hinders applying such principled methods to language generation tasks. In this paper, we circumvent this problem by leveraging the concept of textual entailment to evaluate the correctness of the generated sequence, and propose two selective generation algorithms which control the false discovery rate with respect to the textual entailment relation (FDR-E) with a theoretical guarantee: $\texttt{SGen}^{\texttt{Sup}}$ and $\texttt{SGen}^{\texttt{Semi}}$. $\texttt{SGen}^{\texttt{Sup}}$, a direct modification of the selective prediction, is a supervised learning algorithm which exploits entailment-labeled data, annotated by humans. Since human annotation is costly, we further propose a semi-supervised version, $\texttt{SGen}^{\texttt{Semi}}$, which fully utilizes the unlabeled data by pseudo-labeling, leveraging an entailment set function learned via conformal prediction. Furthermore, $\texttt{SGen}^{\texttt{Semi}}$ enables to use more general class of selection functions, neuro-selection functions, and provides users with an optimal selection function class given multiple candidates. Finally, we demonstrate the efficacy of the $\texttt{SGen}$ family in achieving a desired FDR-E level with comparable selection efficiency to those from baselines on both open and closed source GLMs. Code and datasets are provided at https://github.com/ml-postech/selective-generation. | Selective Generation for Controllable Language Models | [
"Minjae Lee",
"Kyungmin Kim",
"Taesoo Kim",
"Sangdon Park"
] | NeurIPS.cc/2024/Conference | 2307.09254 | [
""
] | https://huggingface.co/papers/2307.09254 | 0 | 1 | 0 | 2 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=glGeXu1zG4 | @inproceedings{
kang2024learning,
title={Learning to Understand: Identifying Interactions via the M\"obius Transform},
author={Justin Singh Kang and Yigit Efe Erginbas and Landon Butler and Ramtin Pedarsani and Kannan Ramchandran},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=glGeXu1zG4}
} | One of the key challenges in machine learning is to find interpretable representations of learned functions. The Möbius transform is essential for this purpose, as its coefficients correspond to unique *importance scores* for *sets of input variables*. This transform is closely related to widely used game-theoretic notions of importance like the *Shapley* and *Bhanzaf value*, but it also captures crucial higher-order interactions. Although computing the Möbius Transform of a function with $n$ inputs involves $2^n$ coefficients, it becomes tractable when the function is *sparse* and of *low-degree* as we show is the case for many real-world functions. Under these conditions, the complexity of the transform computation is significantly reduced. When there are $K$ non-zero coefficients, our algorithm recovers the Möbius transform in $O(Kn)$ samples and $O(Kn^2)$ time asymptotically under certain assumptions, the first non-adaptive algorithm to do so. We also uncover a surprising connection between group testing and the Möbius transform. For functions where all interactions involve at most $t$ inputs, we use group testing results to compute the Möbius transform with $O(Kt\log n)$ sample complexity and $O(K\mathrm{poly}(n))$ time. A robust version of this algorithm withstands noise and maintains this complexity. This marks the first $n$ sub-linear query complexity, noise-tolerant algorithm for the M\"{o}bius transform. While our algorithms are conceptualized in an idealized setting, they indicate that the Möbius transform is a potent tool for interpreting deep learning models. | Learning to Understand: Identifying Interactions via the Möbius Transform | [
"Justin Singh Kang",
"Yigit Efe Erginbas",
"Landon Butler",
"Ramtin Pedarsani",
"Kannan Ramchandran"
] | NeurIPS.cc/2024/Conference | 2402.02631 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=gktA1Qycj9 | @inproceedings{
fang2024cigtime,
title={CigTime: Corrective Instruction Generation Through Inverse Motion Editing},
author={Qihang Fang and Chengcheng Tang and Bugra Tekin and Yanchao Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gktA1Qycj9}
} | Recent advancements in models linking natural language with human motions have shown significant promise in motion generation and editing based on instructional text. Motivated by applications in sports coaching and motor skill learning, we investigate the inverse problem: generating corrective instructional text, leveraging motion editing and generation models. We introduce a novel approach that, given a user’s current motion (source) and the desired motion (target), generates text instructions to guide the user towards achieving the target motion. We leverage large language models to generate corrective texts and utilize existing motion generation and editing frameworks to compile datasets of triplets (source motion, target motion, and corrective text). Using this data, we propose a new motion-language model for generating corrective instructions. We present both qualitative and quantitative results across a diverse range of applications that largely improve upon baselines. Our approach demonstrates its effectiveness in instructional scenarios, offering text-based guidance to correct and enhance user performance. | CigTime: Corrective Instruction Generation Through Inverse Motion Editing | [
"Qihang Fang",
"Chengcheng Tang",
"Bugra Tekin",
"Yanchao Yang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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