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null | https://openreview.net/forum?id=PAiGHJppam | @inproceedings{
zhang2024functionally,
title={Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem},
author={Huaqing Zhang and Lesi Chen and Jing Xu and Jingzhao Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=PAiGHJppam}
} | This paper studies simple bilevel problems, where a convex upper-level function is minimized over the optimal solutions of a convex lower-level problem. We first show the fundamental difficulty of simple bilevel problems, that the approximate optimal value of such problems is not obtainable by first-order zero-respecting algorithms. Then we follow recent works to pursue the weak approximate solutions. For this goal, we propose novel near-optimal methods for smooth and nonsmooth problems by reformulating them into functionally constrained problems. | Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem | [
"Huaqing Zhang",
"Lesi Chen",
"Jing Xu",
"Jingzhao Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=PAWQvrForJ | @inproceedings{
fu2024membership,
title={Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration},
author={Wenjie Fu and Huandong Wang and Chen Gao and Guanghua Liu and Yong Li and Tao Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=PAWQvrForJ}
} | Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Existing MIAs designed for large language models (LLMs) can be bifurcated into two types: reference-free and reference-based attacks. Although reference-based attacks appear promising performance by calibrating the probability measured on the target model with reference models, this illusion of privacy risk heavily depends on a reference dataset that closely resembles the training set. Both two types of attacks are predicated on the hypothesis that training records consistently maintain a higher probability of being sampled. However, this hypothesis heavily relies on the overfitting of target models, which will be mitigated by multiple regularization methods and the generalization of LLMs. Thus, these reasons lead to high false-positive rates of MIAs in practical scenarios.
We propose a Membership Inference Attack based on Self-calibrated Probabilistic Variation (SPV-MIA).
Specifically, we introduce a self-prompt approach, which constructs the dataset to fine-tune the reference model by prompting the target LLM itself. In this manner, the adversary can collect a dataset with a similar distribution from public APIs.
Furthermore, we introduce probabilistic variation, a more reliable membership signal based on LLM memorization rather than overfitting, from which we rediscover the neighbour attack with theoretical grounding.
Comprehensive evaluation conducted on three datasets and four exemplary LLMs shows that SPV-MIA raises the AUC of MIAs from 0.7 to a significantly high level of 0.9. Our code and dataset are available at: https://github.com/tsinghua-fib-lab/NeurIPS2024_SPV-MIA | Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration | [
"Wenjie Fu",
"Huandong Wang",
"Chen Gao",
"Guanghua Liu",
"Yong Li",
"Tao Jiang"
] | NeurIPS.cc/2024/Conference | [
"https://github.com/wjfu99/MIA-LLMs"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=P8rTCT6g45 | @inproceedings{
liang2024memoryefficient,
title={Memory-Efficient {LLM} Training with Online Subspace Descent},
author={Kaizhao Liang and Bo Liu and Lizhang Chen and qiang liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=P8rTCT6g45}
} | Recently, a wide range of memory-efficient LLM training algorithms have gained substantial popularity. These methods leverage the low-rank structure of gradients to project optimizer states into a subspace using projection matrix found by singular value decomposition (SVD). However, convergence of these algorithms is highly dependent on the update rules of their projection matrix. In this work, we provide the \emph{first} convergence guarantee for arbitrary update rules of projection matrix. This guarantee is generally applicable to optimizers that can be analyzed with Hamiltonian Descent, including most common ones, such as LION, Adam. Inspired by our theoretical understanding, we propose Online Subspace Descent, a new family of subspace descent optimizer without SVD. Instead of updating projection matrix with eigenvectors, Online Subspace Descent updates projection matrix wtih online PCA. Online Subspace Descent is flexible and introduces only minimum overhead to training. We demonstrate that, for the task of pretraining LLaMA models ranging from 60M to 1B parameters on the C4 dataset, Online Subspace Descent achieves lower perplexity than state-of-the-art low-rank training methods across different settings and narrows the gap with full-rank baselines. | Memory-Efficient LLM Training with Online Subspace Descent | [
"Kaizhao Liang",
"Bo Liu",
"Lizhang Chen",
"qiang liu"
] | NeurIPS.cc/2024/Conference | 2408.12857 | [
"https://github.com/kyleliang919/online-subspace-descent"
] | https://huggingface.co/papers/2408.12857 | 0 | 12 | 3 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=P6nVDZRZRB | @inproceedings{
shen2024are,
title={Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?},
author={Maohao Shen and Jongha Jon Ryu and Soumya Ghosh and Yuheng Bu and Prasanna Sattigeri and Subhro Das and Gregory W. Wornell},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=P6nVDZRZRB}
} | This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called *evidential deep learning* (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function. Despite their perceived strong empirical performance on downstream tasks, a line of recent studies by Bengs et al. identify limitations of the existing methods to conclude their learned epistemic uncertainties are unreliable, e.g., in that they are non-vanishing even with infinite data. Building on and sharpening such analysis, we 1) provide a sharper understanding of the asymptotic behavior of a wide class of EDL methods by unifying various objective functions; 2) reveal that the EDL methods can be better interpreted as an out-of-distribution detection algorithm based on energy-based-models; and 3) conduct extensive ablation studies to better assess their empirical effectiveness with real-world datasets.
Through all these analyses, we conclude that even when EDL methods are empirically effective on downstream tasks, this occurs despite their poor uncertainty quantification capabilities. Our investigation suggests that incorporating model uncertainty can help EDL methods faithfully quantify uncertainties and further improve performance on representative downstream tasks, albeit at the cost of additional computational complexity. | Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? | [
"Maohao Shen",
"Jongha Jon Ryu",
"Soumya Ghosh",
"Yuheng Bu",
"Prasanna Sattigeri",
"Subhro Das",
"Gregory W. Wornell"
] | NeurIPS.cc/2024/Conference | 2402.06160 | [
"https://github.com/maohaos2/edl-mirage"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=P6aJ7BqYlc | @inproceedings{
zhuang2024gacl,
title={{GACL}: Exemplar-Free Generalized Analytic Continual Learning},
author={Huiping Zhuang and Yizhu Chen and Di Fang and Run He and Kai Tong and Hongxin Wei and Ziqian Zeng and Cen Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=P6aJ7BqYlc}
} | Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks. The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution. Existing attempts for the GCIL either have poor performance or invade data privacy by saving exemplars. In this paper, we propose a new exemplar-free GCIL technique named generalized analytic continual learning (GACL). The GACL adopts analytic learning (a gradient-free training technique) and delivers an analytical (i.e., closed-form) solution to the GCIL scenario. This solution is derived via decomposing the incoming data into exposed and unexposed classes, thereby attaining a weight-invariant property, a rare yet valuable property supporting an equivalence between incremental learning and its joint training. Such an equivalence is crucial in GCIL settings as data distributions among different tasks no longer pose challenges to adopting our GACL. Theoretically, this equivalence property is validated through matrix analysis tools. Empirically, we conduct extensive experiments where, compared with existing GCIL methods, our GACL exhibits a consistently leading performance across various datasets and GCIL settings. Source code is available at https://github.com/CHEN-YIZHU/GACL. | GACL: Exemplar-Free Generalized Analytic Continual Learning | [
"Huiping Zhuang",
"Yizhu Chen",
"Di Fang",
"Run He",
"Kai Tong",
"Hongxin Wei",
"Ziqian Zeng",
"Cen Chen"
] | NeurIPS.cc/2024/Conference | 2403.15706 | [
"https://github.com/ZHUANGHP/Analytic-continual-learning"
] | https://huggingface.co/papers/2403.15706 | 0 | 0 | 0 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=P5yezHuMSS | @inproceedings{
peng2024monoculture,
title={Monoculture in Matching Markets},
author={Kenny Peng and Nikhil Garg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=P5yezHuMSS}
} | Algorithmic monoculture arises when many decision-makers rely on the same algorithm to evaluate applicants. An emerging body of work investigates possible harms of this kind of homogeneity, but has been limited by the challenge of incorporating market effects in which the preferences and behavior of many applicants and decision-makers jointly interact to determine outcomes.
Addressing this challenge, we introduce a tractable theoretical model of algorithmic monoculture in a two-sided matching market with many participants. We use the model to analyze outcomes under monoculture (when decision-makers all evaluate applicants using a common algorithm) and under polyculture (when decision-makers evaluate applicants independently). All else equal, monoculture (1) selects less-preferred applicants when noise is well-behaved, (2) matches more applicants to their top choice, though individual applicants may be worse off depending on their value to decision-makers and risk tolerance, and (3) is more robust to disparities in the number of applications submitted. | Monoculture in Matching Markets | [
"Kenny Peng",
"Nikhil Garg"
] | NeurIPS.cc/2024/Conference | 2312.09841 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=P5dEZeECGu | @inproceedings{
dwibedi2024flexcap,
title={FlexCap: Describe Anything in Images in Controllable Detail},
author={Debidatta Dwibedi and Vidhi Jain and Jonathan Tompson and Andrew Zisserman and Yusuf Aytar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=P5dEZeECGu}
} | We introduce FlexCap, a vision-language model that generates region-specific descriptions of varying lengths. FlexCap is trained to produce length-conditioned captions for input boxes, enabling control over information density, with descriptions ranging from concise object labels to detailed captions. To achieve this, we create large-scale training datasets of image region descriptions with varying lengths from captioned web images. We demonstrate FlexCap’s effectiveness in several applications: first, it achieves strong performance in dense captioning tasks on the Visual Genome dataset. Second, we show how FlexCap’s localized descriptions can serve as input to a large language model to create a visual question answering (VQA) system, achieving state-of-the-art zero-shot performance on multiple VQA benchmarks. Our experiments illustrate FlexCap’s utility for tasks including image labeling, object attribute recognition, and visual dialog. Project webpage: https://flex-cap.github.io. | FlexCap: Describe Anything in Images in Controllable Detail | [
"Debidatta Dwibedi",
"Vidhi Jain",
"Jonathan Tompson",
"Andrew Zisserman",
"Yusuf Aytar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=P4s6FUpCbG | @inproceedings{
liu2024dgsenhancer,
title={3{DGS}-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors},
author={Xi Liu and Chaoyi Zhou and Siyu Huang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=P4s6FUpCbG}
} | Novel-view synthesis aims to generate novel views of a scene from multiple input
images or videos, and recent advancements like 3D Gaussian splatting (3DGS)
have achieved notable success in producing photorealistic renderings with efficient
pipelines. However, generating high-quality novel views under challenging settings,
such as sparse input views, remains difficult due to insufficient information in
under-sampled areas, often resulting in noticeable artifacts. This paper presents
3DGS-Enhancer, a novel pipeline for enhancing the representation quality of
3DGS representations. We leverage 2D video diffusion priors to address the
challenging 3D view consistency problem, reformulating it as achieving temporal
consistency within a video generation process. 3DGS-Enhancer restores view-
consistent latent features of rendered novel views and integrates them with the
input views through a spatial-temporal decoder. The enhanced views are then
used to fine-tune the initial 3DGS model, significantly improving its rendering
performance. Extensive experiments on large-scale datasets of unbounded scenes
demonstrate that 3DGS-Enhancer yields superior reconstruction performance and
high-fidelity rendering results compared to state-of-the-art methods. The project
webpage is https://xiliu8006.github.io/3DGS-Enhancer-project. | 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors | [
"Xi Liu",
"Chaoyi Zhou",
"Siyu Huang"
] | NeurIPS.cc/2024/Conference | 2410.16266 | [
""
] | https://huggingface.co/papers/2410.16266 | 2 | 2 | 2 | 3 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=P3v3x7HnV0 | @inproceedings{
mete2024quest,
title={Que{ST}: Self-Supervised Skill Abstractions for Learning Continuous Control},
author={Atharva Mete and Haotian Xue and Albert Wilcox and Yongxin Chen and Animesh Garg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=P3v3x7HnV0}
} | Generalization capabilities, or rather a lack thereof, is one of the most important unsolved problems in the field of robot learning, and while several large scale efforts have set out to tackle this problem, unsolved it remains. In this paper, we hypothesize that learning temporal action abstractions using latent variable models (LVMs), which learn to map data to a compressed latent space and back, is a
promising direction towards low-level skills that can readily be used for new tasks. Although several works have attempted to show this, they have generally been limited by architectures that do not faithfully capture sharable representations. To address this we present Quantized Skill Transformer (QueST), which learns a larger and more flexible latent encoding that is more capable of modeling the breadth of low-level skills necessary for a variety of tasks. To make use of this extra flexibility, QueST imparts causal inductive bias from the action sequence data into the latent space, leading to more semantically useful and transferable representations. We compare to state-of-the-art imitation learning and LVM baselines and see that QueST’s architecture leads to strong performance on several multitask and few-shot learning benchmarks. Further results and videos are available at https://quest-model.github.io. | QueST: Self-Supervised Skill Abstractions for Learning Continuous Control | [
"Atharva Mete",
"Haotian Xue",
"Albert Wilcox",
"Yongxin Chen",
"Animesh Garg"
] | NeurIPS.cc/2024/Conference | 2407.15840 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OycU0bAus6 | @inproceedings{
xu2024denoiserep,
title={DenoiseRep: Denoising Model for Representation Learning},
author={zhengrui Xu and Guan'an Wang and Xiaowen Huang and Jitao Sang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OycU0bAus6}
} | The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, which is defined as *"learning representations (or features) of the data that make it easier to extract useful information when building classifiers or other predictors"*. In this paper, we propose a novel Denoising Model for Representation Learning (*DenoiseRep*) to improve feature discrimination with joint feature extraction and denoising. *DenoiseRep* views each embedding layer in a backbone as a denoising layer, processing the cascaded embedding layers as if we are recursively denoise features step-by-step. This unifies the frameworks of feature extraction and denoising, where the former progressively embeds features from low-level to high-level, and the latter recursively denoises features step-by-step. After that, *DenoiseRep* fuses the parameters of feature extraction and denoising layers, and *theoretically demonstrates* its equivalence before and after the fusion, thus making feature denoising computation-free. *DenoiseRep* is a label-free algorithm that incrementally improves features but also complementary to the label if available. Experimental results on various discriminative vision tasks, including re-identification (Market-1501, DukeMTMC-reID, MSMT17, CUHK-03, vehicleID), image classification (ImageNet, UB200, Oxford-Pet, Flowers), object detection (COCO), image segmentation (ADE20K) show stability and impressive improvements. We also validate its effectiveness on the CNN (ResNet) and Transformer (ViT, Swin, Vmamda) architectures. | DenoiseRep: Denoising Model for Representation Learning | [
"zhengrui Xu",
"Guan'an Wang",
"Xiaowen Huang",
"Jitao Sang"
] | NeurIPS.cc/2024/Conference | 2406.08773 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=Oy2x0Xfx0u | @inproceedings{
pham2024what,
title={What do Graph Neural Networks learn? Insights from Tropical Geometry},
author={Tuan Anh Pham and Vikas Garg},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Oy2x0Xfx0u}
} | Graph neural networks (GNNs) have been analyzed from multiple perspectives, including the WL-hierarchy, which exposes limits on their expressivity to distinguish graphs. However, characterizing the class of functions that they learn has remained unresolved. We address this fundamental question for message passing GNNs under ReLU activations, i.e., the de-facto choice for most GNNs.
We first show that such GNNs learn tropical rational signomial maps or continuous piecewise linear functions, establishing an equivalence with feedforward networks (FNNs). We then elucidate the role of the choice of aggregation and update functions, and derive the first general upper and lower bounds on the geometric complexity (i.e., the number of linear regions), establishing new results for popular architectures such as GraphSAGE and GIN. We also introduce and theoretically analyze several new architectures to illuminate the relative merits of the feedforward and the message passing layers, and the tradeoffs involving depth and number of trainable parameters. Finally, we also characterize the decision boundary for node and graph classification tasks. | What do Graph Neural Networks learn? Insights from Tropical Geometry | [
"Tuan Anh Pham",
"Vikas Garg"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OxcqkYOy8q | @inproceedings{
gupta2024improved,
title={Improved Sample Complexity Bounds for Diffusion Model Training},
author={Shivam Gupta and Aditya Parulekar and Eric Price and Zhiyang Xun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OxcqkYOy8q}
} | Diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability. From a theoretical standpoint, a number of recent works [CCL+23, CCSW22, BBDD24] have studied the iteration complexity of sampling, assuming access to an accurate diffusion model. In this work, we focus on understanding the *sample complexity* of training such a model; how many samples are needed to learn an accurate diffusion model using a sufficiently expressive neural network? Prior work [BMR20] showed bounds polynomial in the dimension, desired Total Variation error, and Wasserstein error. We show an *exponential improvement* in the dependence on Wasserstein error and depth, along with improved dependencies on other relevant parameters. | Improved Sample Complexity Bounds for Diffusion Model Training | [
"Shivam Gupta",
"Aditya Parulekar",
"Eric Price",
"Zhiyang Xun"
] | NeurIPS.cc/2024/Conference | 2311.13745 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OwguhIAh8R | @inproceedings{
jin2024hgdl,
title={{HGDL}: Heterogeneous Graph Label Distribution Learning},
author={Yufei Jin and Heng Lian and Yi He and Xingquan Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OwguhIAh8R}
} | Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification.
This paper advances LDL into graph domains and aims to tackle a novel and fundamental
heterogeneous graph label distribution learning (HGDL) problem.
We argue that
the graph heterogeneity reflected on node types, node attributes, and neighborhood structures can
impose significant challenges for generalizing
LDL onto graphs.
To address the challenges, we propose a new
learning framework with two key components:
1) proactive graph topology homogenization,
and 2) topology and content consistency-aware graph transformer.
Specifically,
the former learns optimal information aggregation between meta-paths, so that the node
heterogeneity can be proactively addressed prior to the succeeding embedding learning; the latter leverages an attention mechanism to learn consistency between meta-path and node attributes, allowing network topology and nodal attributes to be equally emphasized during the label distribution learning. By using KL-divergence and additional constraints, \method~delivers
an end-to-end solution for learning and predicting label distribution for nodes.
Both theoretical and empirical studies substantiate
the effectiveness of our HGDL approach.
Our code and datasets are available at https://github.com/Listener-Watcher/HGDL. | HGDL: Heterogeneous Graph Label Distribution Learning | [
"Yufei Jin",
"Heng Lian",
"Yi He",
"Xingquan Zhu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Ouc1F0Sfb7 | @inproceedings{
xie2024costaware,
title={Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index},
author={Qian Xie and Raul Astudillo and Peter I. Frazier and Ziv Scully and Alexander Terenin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ouc1F0Sfb7}
} | Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly incorporate function evaluation costs into Bayesian optimization policies. To understand how to do so, we develop a previously-unexplored connection between cost-aware Bayesian optimization and the Pandora's Box problem, a decision problem from economics. The Pandora's Box problem admits a Bayesian-optimal solution based on an expression called the Gittins index, which can be reinterpreted as an acquisition function. We study the use of this acquisition function for cost-aware Bayesian optimization, and demonstrate empirically that it performs well, particularly in medium-high dimensions. We further show that this performance carries over to classical Bayesian optimization without explicit evaluation costs. Our work constitutes a first step towards integrating techniques from Gittins index theory into Bayesian optimization. | Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index | [
"Qian Xie",
"Raul Astudillo",
"Peter I. Frazier",
"Ziv Scully",
"Alexander Terenin"
] | NeurIPS.cc/2024/Conference | 2406.20062 | [
"https://github.com/qianjanexie/pandorabayesopt"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OuQYWNuNxm | @inproceedings{
he2024accelerating,
title={Accelerating Relative Entropy Coding with Space Partitioning},
author={Jiajun He and Gergely Flamich and Jos{\'e} Miguel Hern{\'a}ndez-Lobato},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OuQYWNuNxm}
} | Relative entropy coding (REC) algorithms encode a random sample following a target distribution $Q$, using a coding distribution $P$ shared between the sender and receiver. Sadly, general REC algorithms suffer from prohibitive encoding times, at least on the order of $2^{D_{\text{KL}}[Q||P]}$, and faster algorithms are limited to very specific settings. This work addresses this issue by introducing a REC scheme utilizing space partitioning to reduce runtime in practical scenarios. We provide theoretical analyses of our method and demonstrate its effectiveness with both toy examples and practical applications. Notably, our method successfully handles REC tasks with $D_{\text{KL}}[Q||P]$ about three times greater than what previous methods can manage, and reduces the bitrate by approximately 5-15\% in VAE-based lossless compression on MNIST and INR-based lossy compression on CIFAR-10, compared to previous methods, significantly improving the practicality of REC for neural compression. | Accelerating Relative Entropy Coding with Space Partitioning | [
"Jiajun He",
"Gergely Flamich",
"José Miguel Hernández-Lobato"
] | NeurIPS.cc/2024/Conference | 2405.12203 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OuKW8cUiuY | @inproceedings{
cheng2024diffusion,
title={Diffusion Priors for Variational Likelihood Estimation and Image Denoising},
author={Jun Cheng and Shan Tan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OuKW8cUiuY}
} | Real-world noise removal is crucial in low-level computer vision. Due to the remarkable generation capabilities of diffusion models, recent attention has shifted towards leveraging diffusion priors for image restoration tasks. However, existing diffusion priors-based methods either consider simple noise types or rely on approximate posterior estimation, limiting their effectiveness in addressing structured and signal-dependent noise commonly found in real-world images. In this paper, we build upon diffusion priors and propose adaptive likelihood estimation and MAP inference during the reverse diffusion process to tackle real-world noise. We introduce an independent, non-identically distributed likelihood combined with the noise precision (inverse variance) prior and dynamically infer the precision posterior using variational Bayes during the generation process. Meanwhile, we rectify the estimated noise variance through local Gaussian convolution. The final denoised image is obtained by propagating intermediate MAP solutions that balance the updated likelihood and diffusion prior. Additionally, we explore the local diffusion prior inherent in low-resolution diffusion models, enabling direct handling of high-resolution noisy images. Extensive experiments and analyses on diverse real-world datasets demonstrate the effectiveness of our method. Code is available at https://github.com/HUST-Tan/DiffusionVI. | Diffusion Priors for Variational Likelihood Estimation and Image Denoising | [
"Jun Cheng",
"Shan Tan"
] | NeurIPS.cc/2024/Conference | 2410.17521 | [
"https://github.com/hust-tan/diffusionvi"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=OtvNLTWYww | @inproceedings{
wang2024a,
title={A Theoretical Understanding of Self-Correction through In-context Alignment},
author={Yifei Wang and Yuyang Wu and Zeming Wei and Stefanie Jegelka and Yisen Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OtvNLTWYww}
} | Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, i.e., correcting previous responses through self-examination, as seen in models like OpenAI o1. Nevertheless, little is known about how such capabilities arise. In this work, based on a simplified setup akin to an alignment task, we theoretically analyze self-correction from an in-context learning perspective, showing that when LLMs give relatively accurate self-examinations as rewards, they are capable of refining responses in an in-context way. Notably, going beyond previous theories on over-simplified linear transformers, our theoretical construction underpins the roles of several key designs of realistic transformers for self-correction: softmax attention, multi-head attention, and the MLP block. We validate these findings extensively on synthetic datasets. Inspired by these findings, we propose a simple self-correction strategy, Checking as Context (CaC), which finds novel applications in alleviating social bias and defending against LLM jailbreaks. We believe that these findings will inspire further research on understanding, exploiting, and enhancing self-correction for building better foundation models. Code is at https://github.com/yifeiwang77/Self-Correction. | A Theoretical Understanding of Self-Correction through In-context Alignment | [
"Yifei Wang",
"Yuyang Wu",
"Zeming Wei",
"Stefanie Jegelka",
"Yisen Wang"
] | NeurIPS.cc/2024/Conference | 2405.18634 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OtYCp1yfbX | @inproceedings{
biabani2024improved,
title={Improved Guarantees for Fully Dynamic \$k\$-Center Clustering with Outliers in General Metric Spaces},
author={Leyla Biabani and Annika Hennes and Denise La Gordt Dillie and Morteza Monemizadeh and Melanie Schmidt},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OtYCp1yfbX}
} | The metric $k$-center clustering problem with $z$ outliers, also known as $(k,z)$-center clustering,
involves clustering a given point set $P$ in a metric space $(M,d)$ using at most $k$ balls,
minimizing the maximum ball radius while excluding up to $z$ points from the clustering.
This problem holds fundamental significance in various domains such as machine learning,
data mining, and database systems.
This paper addresses the fully dynamic version of the problem, where the point set undergoes continuous updates (insertions and deletions) over time. The objective is to maintain an approximate $(k,z)$-center clustering with efficient update times.
We propose a novel fully dynamic algorithm that maintains a $(4+\epsilon)$-approximate
solution to the $(k,z)$-center clustering problem that covers
all but at most $(1+\epsilon)z$ points at any time in the sequence with probability $1-k/e^{\Omega(\log k)}$.
The algorithm achieves an expected amortized update time of $\mathcal{O}(\epsilon^{-2} k^6\log(k) \log(\Delta))$, and is applicable to general metric spaces.
Our dynamic algorithm presents a significant improvement over the recent dynamic $(14+\epsilon)$-approximation algorithm by Chan, Lattanzi, Sozio, and Wang for this problem. | Improved Guarantees for Fully Dynamic k-Center Clustering with Outliers in General Metric Spaces | [
"Leyla Biabani",
"Annika Hennes",
"Denise La Gordt Dillie",
"Morteza Monemizadeh",
"Melanie Schmidt"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Osh7u2E1kC | @inproceedings{
huang2024leveraging,
title={Leveraging Separated World Model for Exploration in Visually Distracted Environments},
author={Kaichen Huang and Shenghua Wan and Minghao Shao and Hai-Hang Sun and Le Gan and Shuai Feng and De-Chuan Zhan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Osh7u2E1kC}
} | Model-based unsupervised reinforcement learning (URL) has gained prominence for reducing environment interactions and learning general skills using intrinsic rewards. However, distractors in observations can severely affect intrinsic reward estimation, leading to a biased exploration process, especially in environments with visual inputs like images or videos. To address this challenge, we propose a bi-level optimization framework named Separation-assisted eXplorer (SeeX). In the inner optimization, SeeX trains a separated world model to extract exogenous and endogenous information, minimizing uncertainty to ensure task relevance. In the outer optimization, it learns a policy on imaginary trajectories generated within the endogenous state space to maximize task-relevant uncertainty. Evaluations on multiple locomotion and manipulation tasks demonstrate SeeX's effectiveness. | Leveraging Separated World Model for Exploration in Visually Distracted Environments | [
"Kaichen Huang",
"Shenghua Wan",
"Minghao Shao",
"Hai-Hang Sun",
"Le Gan",
"Shuai Feng",
"De-Chuan Zhan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OrtN9hPP7V | @inproceedings{
huang2024the,
title={The {GAN} is dead; long live the {GAN}! A Modern {GAN} Baseline},
author={Nick Huang and Aaron Gokaslan and Volodymyr Kuleshov and James Tompkin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OrtN9hPP7V}
} | There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks. We provide evidence against this claim and build a modern GAN baseline in a more principled manner. First, we derive a well-behaved regularized relativistic GAN loss that addresses issues of mode dropping and non-convergence that were previously tackled via a bag of ad-hoc tricks. We analyze our loss mathematically and prove that it admits local convergence guarantees, unlike most existing relativistic losses. Second, this loss allows us to discard all ad-hoc tricks and replace outdated backbones used in common GANs with modern architectures. Using StyleGAN2 as an example, we present a roadmap of simplification and modernization that results in a new minimalist baseline---R3GAN. Despite being simple, our approach surpasses StyleGAN2 on FFHQ, ImageNet, CIFAR, and Stacked MNIST datasets, and compares favorably against state-of-the-art GANs and diffusion models. Code: https://www.github.com/brownvc/R3GAN | The GAN is dead; long live the GAN! A Modern GAN Baseline | [
"Nick Huang",
"Aaron Gokaslan",
"Volodymyr Kuleshov",
"James Tompkin"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Oq32ylAOu2 | @inproceedings{
huang2024mindmerger,
title={MindMerger: Efficiently Boosting {LLM} Reasoning in non-English Languages},
author={Zixian Huang and Wenhao Zhu and Gong Cheng and Lei Li and Fei Yuan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Oq32ylAOu2}
} | Reasoning capabilities are crucial for Large Language Models~(LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-tune LLMs to relearn reasoning capabilities in non-English languages, while others replace non-English inputs with an external model's outputs such as English translation text to circumvent the challenge of LLM understanding non-English. Unfortunately, these methods often underutilize the built-in skilled reasoning and useful language understanding capabilities of LLMs. In order to better utilize the minds of reasoning and language understanding in LLMs, we propose a new method, namely MergeMinds, which merges LLMs with the external language understanding capabilities from multilingual models to boost the multilingual reasoning performance. Furthermore, a two-step training scheme is introduced to first train to embeded the external capabilities into LLMs and then train the collaborative utilization of the external capabilities and the built-in capabilities in LLMs. Experiments on three multilingual reasoning datasets and a language understanding dataset demonstrate that MergeMinds consistently outperforms all baselines, especially in low-resource languages. Without updating the parameters of LLMs, the average accuracy improved by 6.7 and 8.0 across all languages and low-resource languages on the MGSM dataset, respectively. | MindMerger: Efficiently Boosting LLM Reasoning in non-English Languages | [
"Zixian Huang",
"Wenhao Zhu",
"Gong Cheng",
"Lei Li",
"Fei Yuan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OoOCoZFVK3 | @inproceedings{
ma2024coevolving,
title={Coevolving with the Other You: Fine-Tuning {LLM} with Sequential Cooperative Multi-Agent Reinforcement Learning},
author={Hao Ma and Tianyi Hu and Zhiqiang Pu and Boyin Liu and Xiaolin Ai and Yanyan Liang and Min Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OoOCoZFVK3}
} | Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominantly rely on PPO and its variants. Though these algorithms are effective in general RL settings, they often exhibit suboptimal performance and vulnerability to distribution collapse when applied to the fine-tuning of LLMs. In this paper, we propose CORY, extending the RL fine-tuning of LLMs to a sequential cooperative multi-agent reinforcement learning framework, to leverage the inherent coevolution and emergent capabilities of multi-agent systems. In CORY, the LLM to be fine-tuned is initially duplicated into two autonomous agents: a pioneer and an observer. The pioneer generates responses based on queries, while the observer generates responses using both the queries and the pioneer’s responses. The two agents are trained together. During training, the agents exchange roles periodically, fostering cooperation and coevolution between them. Experiments evaluate CORY's performance by fine-tuning GPT-2 and Llama-2 under subjective and objective reward functions on the IMDB Review and GSM8K datasets, respectively. Results show that CORY outperforms PPO in terms of policy optimality, resistance to distribution collapse, and training robustness, thereby underscoring its potential as a superior methodology for refining LLMs in real-world applications. | Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning | [
"Hao Ma",
"Tianyi Hu",
"Zhiqiang Pu",
"Boyin Liu",
"Xiaolin Ai",
"Yanyan Liang",
"Min Chen"
] | NeurIPS.cc/2024/Conference | 2410.06101 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Oo7dlLgqQX | @inproceedings{
dominguez-olmedo2024questioning,
title={Questioning the Survey Responses of Large Language Models},
author={Ricardo Dominguez-Olmedo and Moritz Hardt and Celestine Mendler-D{\"u}nner},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Oo7dlLgqQX}
} | Surveys have recently gained popularity as a tool to study large language models. By comparing models’ survey responses to those of different human reference populations, researchers aim to infer the demographics, political opinions, or values best represented by current language models. In this work, we critically examine language models' survey responses on the basis of the well-established American Community Survey by the U.S. Census Bureau. Evaluating 43 different language models using de-facto standard prompting methodologies, we establish two dominant patterns. First, models' responses are governed by ordering and labeling biases, for example, towards survey responses labeled with the letter “A”. Second, when adjusting for these systematic biases through randomized answer ordering, models across the board trend towards uniformly random survey responses, irrespective of model size or training data. As a result, models consistently appear to better represent subgroups whose aggregate statistics are closest to uniform for the survey under consideration, leading to potentially misguided conclusions about model alignment. | Questioning the Survey Responses of Large Language Models | [
"Ricardo Dominguez-Olmedo",
"Moritz Hardt",
"Celestine Mendler-Dünner"
] | NeurIPS.cc/2024/Conference | 2306.07951 | [
"https://github.com/socialfoundations/surveying-language-models"
] | https://huggingface.co/papers/2306.07951 | 0 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | oral |
null | https://openreview.net/forum?id=Oo7HY9kmK6 | @inproceedings{
wang2024meanfield,
title={Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach},
author={Guillaume Wang and Alireza Mousavi-Hosseini and L{\'e}na{\"\i}c Chizat},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Oo7HY9kmK6}
} | Mean-field Langevin dynamics (MLFD) is a class of interacting particle methods that tackle convex optimization over probability measures on a manifold, which are scalable, versatile, and enjoy computational guarantees. However, some important problems -- such as risk minimization for infinite width two-layer neural networks, or sparse deconvolution -- are originally defined over the set of signed, rather than probability, measures. In this paper, we investigate how to extend the MFLD framework to convex optimization problems over signed measures.
Among two known reductions from signed to probability measures -- the lifting and the bilevel approaches -- we show that the bilevel reduction leads to stronger guarantees and faster rates (at the price of a higher per-iteration complexity).
In particular, we investigate the convergence rate of MFLD applied to the bilevel reduction in the low-noise regime and obtain two results. First, this dynamics is amenable to an annealing schedule, adapted from [Suzuki et al., 2023], that results in polynomial convergence rates to a fixed multiplicative accuracy. Second, we investigate the problem of learning a single neuron with the bilevel approach and obtain local exponential convergence rates that depend polynomially on the dimension and noise level (to compare with the exponential dependence that would result from prior analyses). | Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach | [
"Guillaume Wang",
"Alireza Mousavi-Hosseini",
"Lénaïc Chizat"
] | NeurIPS.cc/2024/Conference | 2406.17054 | [
"https://github.com/mousavih/2024-MFLD-bilevel"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=On5WIN7xyD | @inproceedings{
ruan2024observational,
title={Observational Scaling Laws and the Predictability of Langauge Model Performance},
author={Yangjun Ruan and Chris J. Maddison and Tatsunori Hashimoto},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=On5WIN7xyD}
} | Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but the requirement of training models across many different scales has limited their use. We propose an alternative, observational approach that bypasses model training and instead builds scaling laws from ~100 publically available models. Building a single scaling law from multiple model families is challenging due to large variations in their training compute efficiencies and capabilities. However, we show that these variations are consistent with a simple, generalized scaling law where language model performance is a function of a low-dimensional capability space, and model families only vary in their efficiency in converting training compute to capabilities. Using this approach, we show the surprising predictability of complex scaling phenomena: we show that several emergent phenomena follow a smooth, sigmoidal behavior and are predictable from small models; we show that the agent performance of models such as GPT-4 can be precisely predicted from simpler non-agentic benchmarks; and we show how to predict the impact of post-training interventions like Chain-of-Thought and Self-Consistency as language model capabilities continue to improve. | Observational Scaling Laws and the Predictability of Langauge Model Performance | [
"Yangjun Ruan",
"Chris J. Maddison",
"Tatsunori Hashimoto"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=Ok6jSSxzfj | @inproceedings{
tan2024rle,
title={{RLE}: A Unified Perspective of Data Augmentation for Cross-Spectral Re-Identification},
author={Lei Tan and Yukang Zhang and Keke Han and Pingyang Dai and Yan Zhang and YONGJIAN WU and Rongrong Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ok6jSSxzfj}
} | This paper makes a step towards modeling the modality discrepancy in the cross-spectral re-identification task. Based on the Lambertain model, we observe that the non-linear modality discrepancy mainly comes from diverse linear transformations acting on the surface of different materials. From this view, we unify all data augmentation strategies for cross-spectral re-identification as mimicking such local linear transformations and categorize them into moderate transformation and radical transformation. By extending the observation, we propose a Random Linear Enhancement (RLE) strategy which includes Moderate Random Linear Enhancement (MRLE) and Radical Random Linear Enhancement (RRLE) to push the boundaries of both types of transformation. Moderate Random Linear Enhancement is designed to provide diverse image transformations that satisfy the original linear correlations under constrained conditions, whereas Radical Random Linear Enhancement seeks to generate local linear transformations directly without relying on external information. The experimental results not only demonstrate the superiority and effectiveness of RLE but also confirm its great potential as a general-purpose data augmentation for cross-spectral re-identification. | RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-Identification | [
"Lei Tan",
"Yukang Zhang",
"Keke Han",
"Pingyang Dai",
"Yan Zhang",
"YONGJIAN WU",
"Rongrong Ji"
] | NeurIPS.cc/2024/Conference | 2411.01225 | [
"https://github.com/stone96123/rle"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OiVxYf9trg | @inproceedings{
karagodin2024clustering,
title={Clustering in Causal Attention Masking},
author={Nikita Karagodin and Yury Polyanskiy and Philippe Rigollet},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OiVxYf9trg}
} | This work presents a modification of the self-attention dynamics proposed in Geshkovski et al to better reflect the practically relevant, causally masked attention used in transformer architectures for generative AI. This modification translates into an interacting particle system that cannot be interpreted as a mean-field gradient flow. Despite this loss of structure, we significantly strengthen the results of Geshkovski et al in this context: While previous rigorous results focused on cases where all three matrices (key, query, and value) were scaled identities, we prove asymptotic convergence to a single cluster for arbitrary key-query matrices and value matrix equal to the identity.
Additionally, we establish a connection to the classical R\'enyi parking problem from combinatorial geometry to make initial theoretical steps towards demonstrating the existence of meta-stable states. | Clustering in Causal Attention Masking | [
"Nikita Karagodin",
"Yury Polyanskiy",
"Philippe Rigollet"
] | NeurIPS.cc/2024/Conference | 2411.04990 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OgnYoIxtIN | @inproceedings{
ingebrand2024zeroshot,
title={Zero-Shot Transfer of Neural {ODE}s},
author={Tyler Ingebrand and Adam Thorpe and ufuk topcu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OgnYoIxtIN}
} | Autonomous systems often encounter environments and scenarios beyond the scope of their training data, which underscores a critical challenge: the need to generalize and adapt to unseen scenarios in real time. This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer. To this end, we leverage the theory of function encoders, which enables zero-shot transfer by combining the flexibility of neural networks with the mathematical principles of Hilbert spaces. Using this theory, we first present a method for learning a space of dynamics spanned by a set of neural ODE basis functions. After training, the proposed approach can rapidly identify dynamics in the learned space using an efficient inner product calculation. Critically, this calculation requires no gradient calculations or retraining during the online phase. This method enables zero-shot transfer for autonomous systems at runtime and opens the door for a new class of adaptable control algorithms. We demonstrate state-of-the-art system modeling accuracy for two MuJoCo robot environments and show that the learned models can be used for more efficient MPC control of a quadrotor. | Zero-Shot Transfer of Neural ODEs | [
"Tyler Ingebrand",
"Adam Thorpe",
"ufuk topcu"
] | NeurIPS.cc/2024/Conference | 2405.08954 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Of4iNAIUSe | @inproceedings{
li2024resourceaware,
title={Resource-Aware Federated Self-Supervised Learning with Global Class Representations},
author={Mingyi Li and Xiao Zhang and Qi Wang and Tengfei LIU and Ruofan Wu and Weiqiang Wang and Fuzhen Zhuang and Hui Xiong and Dongxiao Yu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Of4iNAIUSe}
} | Due to the heterogeneous architectures and class skew, the global representation models training in resource-adaptive federated self-supervised learning face with tricky challenges: $\textit{deviated representation abilities}$ and $\textit{inconsistent representation spaces}$.
In this work, we are the first to propose a multi-teacher knowledge distillation framework, namely $\textit{FedMKD}$, to learn global representations with whole class knowledge from heterogeneous clients even under extreme class skew. Firstly, the adaptive knowledge integration mechanism is designed to learn better representations from all heterogeneous models with deviated representation abilities. Then the weighted combination of the self-supervised loss and the distillation loss can support the global model to encode all classes from clients into a unified space. Besides, the global knowledge anchored alignment module can make the local representation spaces close to the global spaces, which further improves the representation abilities of local ones. Finally, extensive experiments conducted on two datasets demonstrate the effectiveness of $\textit{FedMKD}$ which outperforms state-of-the-art baselines 4.78\% under linear evaluation on average. | Resource-Aware Federated Self-Supervised Learning with Global Class Representations | [
"Mingyi Li",
"Xiao Zhang",
"Qi Wang",
"Tengfei LIU",
"Ruofan Wu",
"Weiqiang Wang",
"Fuzhen Zhuang",
"Hui Xiong",
"Dongxiao Yu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OesteJF0ls | @inproceedings{
panos2024decomposable,
title={Decomposable Transformer Point Processes},
author={Aristeidis Panos},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OesteJF0ls}
} | The standard paradigm of modeling marked point processes is by parameterizing the intensity function using an attention-based (Transformer-style) architecture. Despite the flexibility of these methods, their inference is based on the computationally intensive thinning algorithm. In this work, we propose a framework where the advantages of the attention-based architecture are maintained and the limitation of the thinning algorithm is circumvented. The framework depends on modeling the conditional distribution of inter-event times with a mixture of log-normals satisfying a Markov property and the conditional probability mass function for the marks with a Transformer-based architecture. The proposed method attains state-of-the-art performance in predicting the next event of a sequence given its history. The experiments also reveal the efficacy of the methods that do not rely on the thinning algorithm during inference over the ones they do. Finally, we test our method on the challenging long-horizon prediction task and find that it outperforms a baseline developed specifically for tackling this task; importantly, inference requires just a fraction of time compared to the thinning-based baseline. | Decomposable Transformer Point Processes | [
"Aristeidis Panos"
] | NeurIPS.cc/2024/Conference | 2409.18158 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OdJKB9jSa5 | @inproceedings{
xia2024stk,
title={{ST}\$\_k\$: A Scalable Module for Solving Top-k Problems},
author={Hanchen Xia and Weidong Liu and Xiaojun Mao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OdJKB9jSa5}
} | The cost of ranking becomes significant in the new stage of deep learning. We propose ST$_k$, a fully differentiable module with a single trainable parameter, designed to solve the Top-k problem without requiring additional time or GPU memory. Due to its fully differentiable nature, ST$_k$ can be embedded end-to-end into neural networks and optimize the Top-k problems within a unified computational graph. We apply ST$_k$ to the Average Top-k Loss (AT$_k$), which inherently faces a Top-k problem. The proposed ST$_k$ Loss outperforms AT$_k$ Loss and achieves the best average performance on multiple benchmarks, with the lowest standard deviation. With the assistance of ST$_k$ Loss, we surpass the state-of-the-art (SOTA) on both CIFAR-100-LT and Places-LT leaderboards. | ST_k: A Scalable Module for Solving Top-k Problems | [
"Hanchen Xia",
"Weidong Liu",
"Xiaojun Mao"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OcO2XakUUK | @inproceedings{
mao2024realizable,
title={Realizable \$H\$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer},
author={Anqi Mao and Mehryar Mohri and Yutao Zhong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OcO2XakUUK}
} | We present a comprehensive study of surrogate loss functions for learning to defer. We introduce a broad family of surrogate losses, parameterized by a non-increasing function $\Psi$, and establish their realizable $H$-consistency under mild conditions. For cost functions based on classification error, we further show that these losses admit $H$-consistency bounds when the hypothesis set is symmetric and complete, a property satisfied by common neural network and linear function hypothesis sets. Our results also resolve an open question raised in previous work [Mozannar et al., 2023] by proving the realizable $H$-consistency and Bayes-consistency of a specific surrogate loss. Furthermore, we identify choices of $\Psi$ that lead to $H$-consistent surrogate losses for *any general cost function*, thus achieving Bayes-consistency, realizable $H$-consistency, and $H$-consistency bounds *simultaneously*. We also investigate the relationship between $H$-consistency bounds and realizable $H$-consistency in learning to defer, highlighting key differences from standard classification. Finally, we empirically evaluate our proposed surrogate losses and compare them with existing baselines. | Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ObUjBHBx8O | @inproceedings{
han2024mitigating,
title={Mitigating Spurious Correlations via Disagreement Probability},
author={Hyeonggeun Han and Sehwan Kim and Hyungjun Joo and Sangwoo Hong and Jungwoo Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ObUjBHBx8O}
} | Models trained with empirical risk minimization (ERM) are prone to be biased towards spurious correlations between target labels and bias attributes, which leads to poor performance on data groups lacking spurious correlations. It is particularly challenging to address this problem when access to bias labels is not permitted. To mitigate the effect of spurious correlations without bias labels, we first introduce a novel training objective designed to robustly enhance model performance across all data samples, irrespective of the presence of spurious correlations. From this objective, we then derive a debiasing method, Disagreement Probability based Resampling for debiasing (DPR), which does not require bias labels. DPR leverages the disagreement between the target label and the prediction of a biased model to identify bias-conflicting samples—those without spurious correlations—and upsamples them according to the disagreement probability. Empirical evaluations on multiple benchmarks demonstrate that DPR achieves state-of-the-art performance over existing baselines that do not use bias labels. Furthermore, we provide a theoretical analysis that details how DPR reduces dependency on spurious correlations. | Mitigating Spurious Correlations via Disagreement Probability | [
"Hyeonggeun Han",
"Sehwan Kim",
"Hyungjun Joo",
"Sangwoo Hong",
"Jungwoo Lee"
] | NeurIPS.cc/2024/Conference | 2411.01757 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OYmms5Mv9H | @inproceedings{
han2024geometric,
title={Geometric Trajectory Diffusion Models},
author={Jiaqi Han and Minkai Xu and Aaron Lou and Haotian Ye and Stefano Ermon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OYmms5Mv9H}
} | Generative models have shown great promise in generating 3D geometric systems, which is a fundamental problem in many natural science domains such as molecule and protein design. However, existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. In this work, we propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories. Modeling such distribution is challenging as it requires capturing both the complex spatial interactions with physical symmetries and temporal correspondence encapsulated in the dynamics. We theoretically justify that diffusion models with equivariant temporal kernels can lead to density with desired symmetry, and develop a novel transition kernel leveraging SE(3)-equivariant spatial convolution and temporal attention. Furthermore, to induce an expressive trajectory distribution for conditional generation, we introduce a generalized learnable geometric prior into the forward diffusion process to enhance temporal conditioning. We conduct extensive experiments on both unconditional and conditional generation in various scenarios, including physical simulation, molecular dynamics, and pedestrian motion. Empirical results on a wide suite of metrics demonstrate that GeoTDM can generate realistic geometric trajectories with significantly higher quality. | Geometric Trajectory Diffusion Models | [
"Jiaqi Han",
"Minkai Xu",
"Aaron Lou",
"Haotian Ye",
"Stefano Ermon"
] | NeurIPS.cc/2024/Conference | 2410.13027 | [
"https://github.com/hanjq17/geotdm"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OYOkkqRLvj | @inproceedings{
li2024amortized,
title={Amortized Eigendecomposition for Neural Networks},
author={Tianbo Li and Zekun Shi and Jiaxi Zhao and Min Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OYOkkqRLvj}
} | Performing eigendecomposition during neural network training is essential for tasks such as dimensionality reduction, network compression, image denoising, and graph learning. However, eigendecomposition is computationally expensive as it is orders of magnitude slower than other neural network operations. To address this challenge, we propose a novel approach called "amortized eigendecomposition" that relaxes the exact eigendecomposition by introducing an additional loss term called eigen loss. Our approach offers significant speed improvements by replacing the computationally expensive eigendecomposition with a more affordable QR decomposition at each iteration. Theoretical analysis guarantees that the desired eigenpair is attained as optima of the eigen loss. Empirical studies on nuclear norm regularization, latent-space principal component analysis, and graphs adversarial learning demonstrate significant improvements in training efficiency while producing nearly identical outcomes to conventional approaches. This novel methodology promises to integrate eigendecomposition efficiently into neural network training, overcoming existing computational challenges and unlocking new potential for advanced deep learning applications. | Amortized Eigendecomposition for Neural Networks | [
"Tianbo Li",
"Zekun Shi",
"Jiaxi Zhao",
"Min Lin"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OX4yll3X53 | @inproceedings{
makkuva2024local,
title={Local to Global: Learning Dynamics and Effect of Initialization for Transformers},
author={Ashok Vardhan Makkuva and Marco Bondaschi and Adway Girish and Alliot Nagle and Hyeji Kim and Michael Gastpar and Chanakya Ekbote},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OX4yll3X53}
} | In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling. To better understand this phenomenon, there is a growing interest in using Markov input processes to study transformers. However, our current understanding in this regard remains limited with many fundamental questions about how transformers learn Markov chains still unanswered. In this paper, we address this by focusing on first-order Markov chains and single-layer transformers, providing a comprehensive characterization of the learning dynamics in this context. Specifically, we prove that transformer parameters trained on next-token prediction loss can either converge to global or local minima, contingent on the initialization and the Markovian data properties, and we characterize the precise conditions under which this occurs. To the best of our knowledge, this is the first result of its kind highlighting the role of initialization. We further demonstrate that our theoretical findings are corroborated by empirical evidence. Based on these insights, we provide guidelines for the initialization of single-layer transformers and demonstrate their effectiveness. Finally, we outline several open problems in this arena. Code is available at: \url{https://github.com/Bond1995/Markov}. | Local to Global: Learning Dynamics and Effect of Initialization for Transformers | [
"Ashok Vardhan Makkuva",
"Marco Bondaschi",
"Adway Girish",
"Alliot Nagle",
"Hyeji Kim",
"Michael Gastpar",
"Chanakya Ekbote"
] | NeurIPS.cc/2024/Conference | 2406.03072 | [
"https://github.com/bond1995/markov"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OWwdlxwnFN | @inproceedings{
le2024monkeysee,
title={MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity},
author={Lynn Le and Paolo Papale and K. Seeliger and Antonio Lozano and Thirza Dado and Feng Wang and Pieter R. Roelfsema and Marcel van Gerven and Ya{\u{g}}mur G{\"u}{\c{c}}l{\"u}t{\"u}rk and Umut G{\"u}{\c{c}}l{\"u}},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OWwdlxwnFN}
} | In this paper, we reconstruct naturalistic images directly from macaque brain signals using a convolutional neural network (CNN) based decoder. We investigate the ability of this CNN-based decoding technique to differentiate among neuronal populations from areas V1, V4, and IT, revealing distinct readout characteristics for each. This research marks a progression from low-level to high-level brain signals, thereby enriching the existing framework for utilizing CNN-based decoders to decode brain activity. Our results demonstrate high-precision reconstructions of naturalistic images, highlighting the efficiency of CNN-based decoders in advancing our knowledge of how the brain's representations translate into pixels. Additionally, we present a novel space-time-resolved decoding technique, demonstrating how temporal resolution in decoding can advance our understanding of neural representations. Moreover, we introduce a learned receptive field layer that sheds light on the CNN-based model's data processing during training, enhancing understanding of its structure and interpretive capacity. | MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity | [
"Lynn Le",
"Paolo Papale",
"K. Seeliger",
"Antonio Lozano",
"Thirza Dado",
"Feng Wang",
"Pieter R. Roelfsema",
"Marcel van Gerven",
"Yağmur Güçlütürk",
"Umut Güçlü"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OWmu3QOa0O | @inproceedings{
dey2024sparse,
title={Sparse maximal update parameterization: A holistic approach to sparse training dynamics},
author={Nolan Simran Dey and Shane Bergsma and Joel Hestness},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OWmu3QOa0O}
} | Several challenges make it difficult for sparse neural networks to compete with dense models. First, setting a large fraction of weights to zero impairs forward and gradient signal propagation. Second, sparse studies often need to test multiple sparsity levels, while also introducing new hyperparameters (HPs), leading to prohibitive tuning costs. Indeed, the standard practice is to re-use the learning HPs originally crafted for dense models. Unfortunately, we show sparse and
dense networks do not share the same optimal HPs. Without stable dynamics and effective training recipes, it is costly to test sparsity at scale, which is key to surpassing dense networks and making the business case for sparsity acceleration in hardware.
A holistic approach is needed to tackle these challenges and we propose S$\textmu$Par as one such approach. For random unstructured static sparsity, S$\textmu$Par ensures activations, gradients, and weight updates all scale independently of sparsity level. Further, by reparameterizing the HPs, S$\textmu$Par enables the same HP values to be optimal as we vary both sparsity level and model width. HPs can be tuned on small dense networks and transferred to large sparse models, greatly reducing tuning costs. On large-scale language modeling, S$\textmu$Par shows increasing improvements over standard parameterization as sparsity increases, leading up to 11.9\% relative loss improvement at 99.2\% sparsity. A minimal implementation of S$\textmu$Par is available at https://github.com/EleutherAI/nanoGPT-mup/tree/supar. | Sparse maximal update parameterization: A holistic approach to sparse training dynamics | [
"Nolan Simran Dey",
"Shane Bergsma",
"Joel Hestness"
] | NeurIPS.cc/2024/Conference | 2405.15743 | [
"https://github.com/eleutherai/nanogpt-mup"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OWPzhVqIux | @inproceedings{
krishnamurthy2024can,
title={Can large language models explore in-context?},
author={Akshay Krishnamurthy and Keegan Harris and Dylan J Foster and Cyril Zhang and Aleksandrs Slivkins},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OWPzhVqIux}
} | We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on native performance of existing LLMs, without training interventions. We deploy LLMs as agents in simple multi-armed bandit environments, specifying the environment description and interaction history entirely in-context, i.e., within the LLM prompt. We experiment with GPT-3.5, GPT-4, and Llama2, using a variety of prompt designs, and find that the models do not robustly engage in exploration without substantial interventions: i) Only one configuration resulted in satisfactory exploratory behavior: GPT-4 with chain-of-thought reasoning and an externally summarized interaction history; ii) All other configurations did not result in robust exploratory behavior, including those with chain-of-thought reasoning but unsummarized history. While these findings can be interpreted positively, they suggest that external summarization—which may not be possible in more complex settings—is essential for desirable LLM behavior. We conclude that non-trivial algorithmic interventions, such as fine-tuning or dataset curation, may be required to empower LLM-based decision making agents in complex settings. | Can large language models explore in-context? | [
"Akshay Krishnamurthy",
"Keegan Harris",
"Dylan J Foster",
"Cyril Zhang",
"Aleksandrs Slivkins"
] | NeurIPS.cc/2024/Conference | 2403.15371 | [
""
] | https://huggingface.co/papers/2403.15371 | 3 | 32 | 2 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=OW1ldvMNJ6 | @inproceedings{
jiang2024comat,
title={CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching},
author={Dongzhi Jiang and Guanglu Song and Xiaoshi Wu and Renrui Zhang and Dazhong Shen and Zhuofan Zong and Yu Liu and Hongsheng Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OW1ldvMNJ6}
} | Diffusion models have demonstrated great success in the field of text-to-image generation. However, alleviating the misalignment between the text prompts and images is still challenging. We break down the problem into two causes: concept ignorance and concept mismapping. To tackle the two challenges, we propose CoMat, an end-to-end diffusion model fine-tuning strategy with the image-to-text concept matching mechanism. Firstly, we introduce a novel image-to-text concept activation module to guide the diffusion model in revisiting ignored concepts. Additionally, an attribute concentration module is proposed to map the text conditions of each entity to its corresponding image area correctly. Extensive experimental evaluations, conducted across three distinct text-to-image alignment benchmarks, demonstrate the superior efficacy of our proposed method, CoMat-SDXL, over the baseline model, SDXL~\cite{podell2023sdxl}. We also show that our method enhances general condition utilization capability and generalizes to the long and complex prompt despite not specifically training on it. | CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching | [
"Dongzhi Jiang",
"Guanglu Song",
"Xiaoshi Wu",
"Renrui Zhang",
"Dazhong Shen",
"Zhuofan Zong",
"Yu Liu",
"Hongsheng Li"
] | NeurIPS.cc/2024/Conference | 2404.03653 | [
"https://github.com/Karine-Huang/T2I-CompBench"
] | https://huggingface.co/papers/2404.03653 | 2 | 33 | 3 | 8 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=OV8YUk151r | @inproceedings{
hao2024hlmcite,
title={{HLM}-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction},
author={Qianyue Hao and Jingyang Fan and Fengli Xu and Jian Yuan and Yong Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OV8YUk151r}
} | Citation networks are critical infrastructures of modern science, serving as intricate webs of past literature and enabling researchers to navigate the knowledge production system. To mine information hiding in the link space of such networks, predicting which previous papers (candidates) will a new paper (query) cite is a critical problem that has long been studied. However, an important gap remains unaddressed: the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts. Distinguishing these roles requires a deeper understanding of the logical relationships among papers, beyond simple edges in citation networks. The emergence of large language models (LLMs) with textual reasoning capabilities offers new possibilities for discerning these relationships, but there are two major challenges. First, in practice, a new paper may select its citations from gigantic existing papers, where the combined texts far exceed the context length of LLMs. Second, logical relationships between papers are often implicit, and directly prompting an LLM to predict citations may lead to results based primarily on surface-level textual similarities, rather than the deeper logical reasoning required. In this paper, we introduce the novel concept of core citation, which identifies the critical references that go beyond superficial mentions. Thereby, we elevate the citation prediction task from a simple binary classification to a more nuanced problem: distinguishing core citations from both superficial citations and non-citations. To address this, we propose $\textbf{HLM-Cite}$, a $\textbf{H}$ybrid $\textbf{L}$anguage $\textbf{M}$odel workflow for citation prediction, which combines embedding and generative LMs. We design a curriculum finetune procedure to adapt a pretrained text embedding model to coarsely retrieve high-likelihood core citations from vast candidate sets and then design an LLM agentic workflow to rank the retrieved papers through one-shot reasoning, revealing the implicit relationships among papers. With the two-stage pipeline, we can scale the candidate sets to 100K papers, vastly exceeding the size handled by existing methods. We evaluate HLM-Cite on a dataset across 19 scientific fields, demonstrating a 17.6\% performance improvement comparing SOTA methods. Our code is open-source at https://github.com/tsinghua-fib-lab/H-LM for reproducibility. | HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction | [
"Qianyue Hao",
"Jingyang Fan",
"Fengli Xu",
"Jian Yuan",
"Yong Li"
] | NeurIPS.cc/2024/Conference | 2410.09112 | [
"https://github.com/tsinghua-fib-lab/H-LM"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OUXnnPJzXJ | @inproceedings{
kong2024perplexityaware,
title={Perplexity-aware Correction for Robust Alignment with Noisy Preferences},
author={Keyi Kong and Xilie Xu and Di Wang and Jingfeng Zhang and Mohan Kankanhalli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OUXnnPJzXJ}
} | Alignment techniques are critical in ensuring that large language models (LLMs) output helpful and harmless content by enforcing the LLM-generated content to align with human preferences.
However, the existence of noisy preferences (NPs), where the responses are mistakenly labelled as chosen or rejected, could spoil the alignment, thus making the LLMs generate useless and even malicious content.
Existing methods mitigate the issue of NPs from the loss perspective by adjusting the alignment loss based on a clean validation dataset.
Orthogonal to these loss-oriented methods, we propose perplexity-aware correction (PerpCorrect) from the data perspective for robust alignment which detects and corrects NPs based on the differences between the perplexity of the chosen and rejected responses (dubbed as PPLDiff).
Intuitively, a higher PPLDiff indicates a higher probability of the NP because a rejected/chosen response which is mistakenly labelled as chosen/rejected is less preferable to be generated by an aligned LLM, thus having a higher/lower perplexity.
PerpCorrect works in three steps:
(1) PerpCorrect aligns a surrogate LLM using the clean validation data to make the PPLDiff able to distinguish clean preferences (CPs) and NPs.
(2) PerpCorrect further aligns the surrogate LLM by incorporating the reliable clean training data whose PPLDiff is extremely small and reliable noisy training data whose PPLDiff is extremely large after correction to boost the discriminatory power.
(3) Detecting and correcting NPs according to the PPLDiff obtained by the aligned surrogate LLM to obtain a denoised training dataset for robust alignment.
Comprehensive experiments validate that our proposed PerpCorrect can achieve state-of-the-art alignment performance under NPs.
Notably, PerpCorrect demonstrates practical utility by requiring only a modest amount of validation data and being compatible with various alignment techniques.
Our code is available at [PerpCorrect](https://github.com/luxinyayaya/PerpCorrect). | Perplexity-aware Correction for Robust Alignment with Noisy Preferences | [
"Keyi Kong",
"Xilie Xu",
"Di Wang",
"Jingfeng Zhang",
"Mohan Kankanhalli"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OU1uqd1vyw | @inproceedings{
zhao2024clues,
title={{CLUES}: Collaborative Private-domain High-quality Data Selection for {LLM}s via Training Dynamics},
author={Wanru Zhao and Hongxiang Fan and Shell Xu Hu and Wangchunshu Zhou and Nicholas Donald Lane},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OU1uqd1vyw}
} | Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collaborative settings where sharing is not allowed directly between data silos. To tackle this issue, this paper proposes a novel data quality control technique based on the notion of data influence on the training dynamics of LLMs, that high quality data are more likely to have similar training dynamics to the anchor dataset. We then leverage the influence of the training dynamics to select high-quality data from different private domains, with centralized model updates on the server side in a collaborative training fashion by either model merging or federated learning. As for the data quality indicator, we compute the per-sample gradients with respect to the private data and the anchor dataset, and use the trace of the accumulated inner products as a measurement of data quality. In addition, we develop a quality control evaluation tailored for collaborative settings with heterogeneous medical domain data. Experiments show that training on the high-quality data selected by our method can often outperform other data selection methods for collaborative fine-tuning of LLMs, across diverse private domain datasets, in medical, multilingual and financial settings. | CLUES: Collaborative Private-domain High-quality Data Selection for LLMs via Training Dynamics | [
"Wanru Zhao",
"Hongxiang Fan",
"Shell Xu Hu",
"Wangchunshu Zhou",
"Nicholas Donald Lane"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OSHaRf4TVU | @inproceedings{
grigsby2024amago,
title={{AMAGO}-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers},
author={Jake Grigsby and Justin Sasek and Samyak Parajuli and Daniel Adebi and Amy Zhang and Yuke Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OSHaRf4TVU}
} | Language models trained on diverse datasets unlock generalization by in-context learning. Reinforcement Learning (RL) policies can achieve a similar effect by meta-learning within the memory of a sequence model. However, meta-RL research primarily focuses on adapting to minor variations of a single task. It is difficult to scale towards more general behavior without confronting challenges in multi-task optimization, and few solutions are compatible with meta-RL's goal of learning from large training sets of unlabeled tasks. To address this challenge, we revisit the idea that multi-task RL is bottlenecked by imbalanced training losses created by uneven return scales across different tasks. We build upon recent advancements in Transformer-based (in-context) meta-RL and evaluate a simple yet scalable solution where both an agent's actor and critic objectives are converted to classification terms that decouple optimization from the current scale of returns. Large-scale comparisons in Meta-World ML45, Multi-Game Procgen, Multi-Task POPGym, Multi-Game Atari, and BabyAI find that this design unlocks significant progress in online multi-task adaptation and memory problems without explicit task labels. | AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers | [
"Jake Grigsby",
"Justin Sasek",
"Samyak Parajuli",
"Daniel Adebi",
"Amy Zhang",
"Yuke Zhu"
] | NeurIPS.cc/2024/Conference | 2411.11188 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=ORQiboaRqY | @inproceedings{
li2024on,
title={On the Power of Small-size Graph Neural Networks for Linear Programming},
author={Qian Li and Tian Ding and Linxin Yang and Minghui Ouyang and Qingjiang Shi and Ruoyu Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ORQiboaRqY}
} | Graph neural networks (GNNs) have recently emerged as powerful tools for addressing complex optimization problems. It has been theoretically demonstrated that GNNs can universally approximate the solution mapping functions of linear programming (LP) problems. However, these theoretical results typically require GNNs to have large parameter sizes. Conversely, empirical experiments have shown that relatively small GNNs can solve LPs effectively, revealing a significant discrepancy between theoretical predictions and practical observations. In this work, we aim to bridge this gap by providing a theoretical foundation for the effectiveness of small-size GNNs. We prove that polylogarithmic-depth, constant-width GNNs are sufficient to solve packing and covering LPs, two widely used classes of LPs. Our proof leverages the capability of GNNs to simulate a variant of the gradient descent algorithm on a carefully selected potential function. Additionally, we introduce a new GNN architecture, termed GD-Net. Experimental results demonstrate that GD-Net significantly outperforms conventional GNN structures while using fewer parameters. | On the Power of Small-size Graph Neural Networks for Linear Programming | [
"Qian Li",
"Tian Ding",
"Linxin Yang",
"Minghui Ouyang",
"Qingjiang Shi",
"Ruoyu Sun"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OQzCSb6fbl | @inproceedings{
lappe2024parallel,
title={Parallel Backpropagation for Shared-Feature Visualization},
author={Alexander Lappe and Anna Bogn{\'a}r and Ghazaleh Ghamkhari Nejad and Albert Mukovskiy and Lucas Martini and Martin A. Giese and Rufin Vogels},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OQzCSb6fbl}
} | High-level visual brain regions contain subareas in which neurons appear to respond more strongly to examples of a particular semantic category, like faces or bodies, rather than objects. However, recent work has shown that while this finding holds on average, some out-of-category stimuli also activate neurons in these regions. This may be due to visual features common among the preferred class also being present in other images. Here, we propose a deep-learning-based approach for visualizing these features. For each neuron, we identify relevant visual features driving its selectivity by modelling responses to images based on latent activations of a deep neural network. Given an out-of-category image which strongly activates the neuron, our method first identifies a reference image from the preferred category yielding a similar feature activation pattern. We then backpropagate latent activations of both images to the pixel level, while enhancing the identified shared dimensions and attenuating non-shared features. The procedure highlights image regions containing shared features driving responses of the model neuron. We apply the algorithm to novel recordings from body-selective regions in macaque IT cortex in order to understand why some images of objects excite these neurons. Visualizations reveal object parts which resemble parts of a macaque body, shedding light on neural preference of these objects. | Parallel Backpropagation for Shared-Feature Visualization | [
"Alexander Lappe",
"Anna Bognár",
"Ghazaleh Ghamkhari Nejad",
"Albert Mukovskiy",
"Lucas Martini",
"Martin A. Giese",
"Rufin Vogels"
] | NeurIPS.cc/2024/Conference | 2405.09827 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=OQUg2T4qJB | @inproceedings{
xu2024orderingbased,
title={Ordering-Based Causal Discovery for Linear and Nonlinear Relations},
author={Zhuopeng Xu and Yujie Li and Cheng Liu and Ning Gui},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OQUg2T4qJB}
} | Identifying causal relations from purely observational data typically requires additional assumptions on relations and/or noise. Most current methods restrict their analysis to datasets that are assumed to have pure linear or nonlinear relations, which is often not reflective of real-world datasets that contain a combination of both. This paper presents CaPS, an ordering-based causal discovery algorithm that effectively handles linear and nonlinear relations. CaPS introduces a novel identification criterion for topological ordering and incorporates the concept of "parent score" during the post-processing optimization stage. These scores quantify the strength of the average causal effect, helping to accelerate the pruning process and correct inaccurate predictions in the pruning step. Experimental results demonstrate that our proposed solutions outperform state-of-the-art baselines on synthetic data with varying ratios of linear and nonlinear relations. The results obtained from real-world data also support the competitiveness of CaPS. Code and datasets are available at https://github.com/E2real/CaPS. | Ordering-Based Causal Discovery for Linear and Nonlinear Relations | [
"Zhuopeng Xu",
"Yujie Li",
"Cheng Liu",
"Ning Gui"
] | NeurIPS.cc/2024/Conference | 2410.05890 | [
"https://github.com/e2real/caps"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OPrPegYIZo | @inproceedings{
liang2024dynamiterl,
title={Dyna{MITE}-{RL}: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning},
author={Anthony Liang and Guy Tennenholtz and ChihWei Hsu and Yinlam Chow and Erdem Biyik and Craig Boutilier},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OPrPegYIZo}
} | We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episode sessions---parts of the episode where the latent state is fixed---and propose three key modifications to existing meta-RL methods: (i) consistency of latent information within sessions, (ii) session masking, and (iii) prior latent conditioning. We demonstrate the importance of these modifications in various domains, ranging from discrete Gridworld environments to continuous-control and simulated robot assistive tasks, illustrating the efficacy of DynaMITE-RL over state-of-the-art baselines in both online and offline RL settings. | DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning | [
"Anthony Liang",
"Guy Tennenholtz",
"ChihWei Hsu",
"Yinlam Chow",
"Erdem Biyik",
"Craig Boutilier"
] | NeurIPS.cc/2024/Conference | 2402.15957 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OP3sNTIE1O | @inproceedings{
ban2024data,
title={Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning},
author={Seonghyun Ban and Heesan Kong and Kee-Eung Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OP3sNTIE1O}
} | Semi-supervised learning (SSL) seeks to utilize unlabeled data to overcome the limited amount of labeled data and improve model performance. However, many SSL methods typically struggle in real-world scenarios, particularly when there is a large number of irrelevant instances in the unlabeled data that do not belong to any class in the labeled data. Previous approaches often downweight instances from irrelevant classes to mitigate the negative impact of class distribution mismatch on model training. However, by discarding irrelevant instances, they may result in the loss of valuable information such as invariance, regularity, and diversity within the data. In this paper, we propose a data-centric generative augmentation approach that leverages a diffusion model to enrich labeled data using both labeled and unlabeled samples. A key challenge is extracting the diversity inherent in the unlabeled data while mitigating the generation of samples irrelevant to the labeled data. To tackle this issue, we combine diffusion model training with a discriminator that identifies and reduces the impact of irrelevant instances. We also demonstrate that such a trained diffusion model can even convert an irrelevant instance into a relevant one, yielding highly effective synthetic data for training. Through a comprehensive suite of experiments, we show that our data augmentation approach significantly enhances the performance of SSL methods, especially in the presence of class distribution mismatch. | Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning | [
"Seonghyun Ban",
"Heesan Kong",
"Kee-Eung Kim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OP2D9sIdo4 | @inproceedings{
wang2024suitable,
title={Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection},
author={Jingjing Wang and Minhuan Huang and Yuanping Nie and Xiang Li and Qianjin Du and Wei Kong and Huan Deng and Xiaohui Kuang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OP2D9sIdo4}
} | Deep learning technologies have demonstrated remarkable performance in vulnerability detection. Existing works primarily adopt a uniform and consistent feature learning pattern across the entire target set. While designed for general-purpose detection tasks, they lack sensitivity towards target code comprising multiple functional modules or diverse vulnerability subtypes. In this paper, we present a knowledge fusion-based vulnerability detection method (KF-GVD) that integrates specific vulnerability knowledge into the Graph Neural Network feature learning process. KF-GVD achieves accurate vulnerability detection across different functional modules of the Linux kernel and vulnerability subtypes without compromising general task performance. Extensive experiments demonstrate that KF-GVD outperforms SOTAs on function-level and statement-level vulnerability detection across various target tasks, with an average increase of 40.9% in precision and 26.1% in recall. Notably, KF-GVD discovered 9 undisclosed vulnerabilities when employing on C/C++ open-source projects without ground truth. | Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection | [
"Jingjing Wang",
"Minhuan Huang",
"Yuanping Nie",
"Xiang Li",
"Qianjin Du",
"Wei Kong",
"Huan Deng",
"Xiaohui Kuang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OOiRS6fiM7 | @inproceedings{
smet2024a,
title={A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetics},
author={Lennert De Smet and Pedro Zuidberg Dos Martires},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OOiRS6fiM7}
} | As illustrated by the success of integer linear programming, linear integer arithmetics is a powerful tool for modelling combinatorial problems. Furthermore, the probabilistic extension of linear programming has been used to formulate problems in neurosymbolic AI. However, two key problems persist that prevent the adoption of neurosymbolic techniques beyond toy problems. First, probabilistic inference is inherently hard, #P-hard to be precise. Second, the discrete nature of integers renders the construction of meaningful gradients challenging, which is problematic for learning. In order to mitigate these issues, we formulate linear arithmetics over integer-valued random variables as tensor manipulations that can be implemented in a straightforward fashion using modern deep learning libraries. At the core of our formulation lies the observation that the addition of two integer-valued random variables can be performed by adapting the fast Fourier transform to probabilities in the log-domain. By relying on tensor operations we obtain a differentiable data structure, which unlocks, virtually for free, gradient-based learning. In our experimental validation we show that tensorising probabilistic integer linear arithmetics and leveraging the fast Fourier transform allows us to push the state of the art by several orders of magnitude in terms of inference and learning times. | A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetics | [
"Lennert De Smet",
"Pedro Zuidberg Dos Martires"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OONojmx3wH | @inproceedings{
hansen2024when,
title={When is Multicalibration Post-Processing Necessary?},
author={Dutch Hansen and Siddartha Devic and Preetum Nakkiran and Vatsal Sharan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OONojmx3wH}
} | Calibration is a well-studied property of predictors which guarantees meaningful uncertainty estimates. Multicalibration is a related notion --- originating in algorithmic fairness --- which requires predictors to be simultaneously calibrated over a potentially complex and overlapping collection of protected subpopulations (such as groups defined by ethnicity, race, or income). We conduct the first comprehensive study evaluating the usefulness of multicalibration post-processing across a broad set of tabular, image, and language datasets for models spanning from simple decision trees to 90 million parameter fine-tuned LLMs. Our findings can be summarized as follows: (1) models which are calibrated out of the box tend to be relatively multicalibrated without any additional post-processing; (2) multicalibration can help inherently uncalibrated models and also large vision and language models; and (3) traditional calibration measures may sometimes provide multicalibration implicitly. More generally, we also distill many independent observations which may be useful for practical and effective applications of multicalibration post-processing in real-world contexts. | When is Multicalibration Post-Processing Necessary? | [
"Dutch Hansen",
"Siddartha Devic",
"Preetum Nakkiran",
"Vatsal Sharan"
] | NeurIPS.cc/2024/Conference | 2406.06487 | [
"https://github.com/dutchhansen/multicalibration"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OJxua0PAIo | @inproceedings{
chae2024stochastic,
title={Stochastic Extragradient with Flip-Flop Shuffling \& Anchoring: Provable Improvements},
author={Jiseok Chae and Chulhee Yun and Donghwan Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OJxua0PAIo}
} | In minimax optimization, the extragradient (EG) method has been extensively studied because it outperforms the gradient descent-ascent method in convex-concave (C-C) problems. Yet, stochastic EG (SEG) has seen limited success in C-C problems, especially for unconstrained cases. Motivated by the recent progress of shuffling-based stochastic methods, we investigate the convergence of shuffling-based SEG in unconstrained finite-sum minimax problems, in search of convergent shuffling-based SEG. Our analysis reveals that both random reshuffling and the recently proposed flip-flop shuffling alone can suffer divergence in C-C problems. However, with an additional simple trick called anchoring, we develop the SEG with flip-flop anchoring (SEG-FFA) method which successfully converges in C-C problems. We also show upper and lower bounds in the strongly-convex-strongly-concave setting, demonstrating that SEG-FFA has a provably faster convergence rate compared to other shuffling-based methods. | Stochastic Extragradient with Flip-Flop Shuffling Anchoring: Provable Improvements | [
"Jiseok Chae",
"Chulhee Yun",
"Donghwan Kim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OJximyClit | @inproceedings{
zhu2024enhancing,
title={Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting},
author={Xingyu Zhu and Beier Zhu and Yi Tan and Shuo Wang and Yanbin Hao and Hanwang Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OJximyClit}
} | Vision-language models, such as CLIP, have shown impressive generalization capacities when using appropriate text descriptions. While optimizing prompts on downstream labeled data has proven effective in improving performance, these methods entail labor costs for annotations and are limited by their quality. Additionally, since CLIP is pre-trained on highly imbalanced Web-scale data, it suffers from inherent label bias that leads to suboptimal performance.
To tackle the above challenges, we propose a label-**F**ree p**ro**mpt distribution **l**earning and b**i**as **c**orrection framework, dubbed as **Frolic**, which boosts zero-shot performance without the need for labeled data. Specifically, our Frolic learns distributions over prompt prototypes to capture diverse visual representations and adaptively fuses these with the original CLIP through confidence matching.
This fused model is further enhanced by correcting label bias via a label-free logit adjustment. Notably, our method is not only training-free but also circumvents the necessity for hyper-parameter tuning. Extensive experimental results across 16 datasets demonstrate the efficacy of our approach, particularly outperforming the state-of-the-art by an average of $2.6\%$ on 10 datasets with CLIP ViT-B/16 and achieving an average margin of $1.5\%$ on ImageNet and its five distribution shifts with CLIP ViT-B/16. Codes are available in [https://github.com/zhuhsingyuu/Frolic](https://github.com/zhuhsingyuu/Frolic). | Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting | [
"Xingyu Zhu",
"Beier Zhu",
"Yi Tan",
"Shuo Wang",
"Yanbin Hao",
"Hanwang Zhang"
] | NeurIPS.cc/2024/Conference | 2410.19294 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=OIsUWQSvkD | @inproceedings{
chen2024identifying,
title={Identifying Causal Effects Under Functional Dependencies},
author={Yizuo Chen and Adnan Darwiche},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OIsUWQSvkD}
} | We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects. | Identifying Causal Effects Under Functional Dependencies | [
"Yizuo Chen",
"Adnan Darwiche"
] | NeurIPS.cc/2024/Conference | 2403.04919 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=OFmclNhp0y | @inproceedings{
akg{\"u}l2024deterministic,
title={Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning},
author={Abdullah Akg{\"u}l and Manuel Haussmann and Melih Kandemir},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OFmclNhp0y}
} | Current approaches to model-based offline reinforcement learning often incorporate uncertainty-based reward penalization to address the distributional shift problem. These approaches, commonly known as pessimistic value iteration, use Monte Carlo sampling to estimate the Bellman target to perform temporal difference based policy evaluation. We find out that the randomness caused by this sampling step significantly delays convergence. We present a theoretical result demonstrating the strong dependency of suboptimality on the number of Monte Carlo samples taken per Bellman target calculation. Our main contribution is a deterministic approximation to the Bellman target that uses progressive moment matching, a method developed originally for deterministic variational inference. The resulting algorithm, which we call Moment Matching Offline Model-Based Policy Optimization (MOMBO), propagates the uncertainty of the next state through a nonlinear Q-network in a deterministic fashion by approximating the distributions of hidden layer activations by a normal distribution. We show that it is possible to provide tighter guarantees for the suboptimality of MOMBO than the existing Monte Carlo sampling approaches. We also observe MOMBO to converge faster than these approaches in a large set of benchmark tasks. | Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning | [
"Abdullah Akgül",
"Manuel Haussmann",
"Melih Kandemir"
] | NeurIPS.cc/2024/Conference | 2406.04088 | [
"https://github.com/adinlab/MOMBO"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OF0YsxoRai | @inproceedings{
wei2024scalable,
title={Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes},
author={Yunyue Wei and Vincent Zhuang and Saraswati Soedarmadji and Yanan Sui},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OF0YsxoRai}
} | Bayesian optimization is an effective technique for black-box optimization, but its applicability is typically limited to low-dimensional and small-budget problems due to the cubic complexity of computing the Gaussian process (GP) surrogate. While various approximate GP models have been employed to scale Bayesian optimization to larger sample sizes, most suffer from overly-smooth estimation and focus primarily on problems that allow for large online samples. In this work, we argue that Bayesian optimization algorithms with sparse GPs can more efficiently allocate their representational power to relevant regions of the search space. To achieve this, we propose focalized GP, which leverages a novel variational loss function to achieve stronger local prediction, as well as FocalBO, which hierarchically optimizes the focalized GP acquisition function over progressively smaller search spaces. Experimental results demonstrate that FocalBO can efficiently leverage large amounts of offline and online data to achieve state-of-the-art performance on robot morphology design and to control a 585-dimensional musculoskeletal system. | Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes | [
"Yunyue Wei",
"Vincent Zhuang",
"Saraswati Soedarmadji",
"Yanan Sui"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OEWBkLrRZu | @inproceedings{
gao2024towards,
title={Towards Stable Representations for Protein Interface Prediction},
author={Ziqi Gao and Zijing Liu and Yu Li and Jia Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OEWBkLrRZu}
} | The knowledge of protein interactions is crucial but challenging for drug discovery applications. This work focuses on protein interface prediction, which aims to determine whether a pair of residues from different proteins interact. Existing data-driven methods have made significant progress in effectively learning protein structures. Nevertheless, they overlook the conformational changes (i.e., flexibility) within proteins upon binding, leading to poor generalization ability. In this paper, we regard the protein flexibility as an attack on the trained model and aim to defend against it for improved generalization. To fulfill this purpose, we propose ATProt, an adversarial training framework for protein representations to robustly defend against the attack of protein flexibility. ATProt can theoretically guarantee protein representation stability under complicated protein flexibility. Experiments on various benchmarks demonstrate that ATProt consistently improves the performance for protein interface prediction. Moreover, our method demonstrates broad applicability, performing the best even when provided with testing structures from structure prediction models like ESMFold and AlphaFold2. | Towards Stable Representations for Protein Interface Prediction | [
"Ziqi Gao",
"Zijing Liu",
"Yu Li",
"Jia Li"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ODbTlAs0Oj | @inproceedings{
luo2024mgpt,
title={M\${\textasciicircum}3\${GPT}: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation},
author={Mingshuang Luo and RuiBing Hou and Zhuo Li and Hong Chang and Zimo Liu and Yaowei Wang and Shiguang Shan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=ODbTlAs0Oj}
} | This paper presents M$^3$GPT, an advanced $\textbf{M}$ultimodal, $\textbf{M}$ultitask framework for $\textbf{M}$otion comprehension and generation. M$^3$GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal conditional signals, such as text, music and motion/dance, enabling seamless integration into a large language model (LLM) with a single vocabulary.
The second involves modeling motion generation directly in the raw motion space. This strategy circumvents the information loss associated with a discrete tokenizer, resulting in more detailed and comprehensive motion generation.
Third, M$^3$GPT learns to model the connections and synergies among various motion-relevant tasks. Text, the most familiar and well-understood modality for LLMs, is utilized as a bridge to establish connections between different motion tasks, facilitating mutual
reinforcement. To our knowledge, M$^3$GPT is the first model capable of comprehending and generating motions based on multiple signals.
Extensive experiments highlight M$^3$GPT's superior performance across various motion-relevant tasks and its powerful zero-shot generalization capabilities for extremely challenging tasks. Project page: \url{https://github.com/luomingshuang/M3GPT}. | M^3GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation | [
"Mingshuang Luo",
"RuiBing Hou",
"Zhuo Li",
"Hong Chang",
"Zimo Liu",
"Yaowei Wang",
"Shiguang Shan"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OCcfKzXded | @inproceedings{
xiong2024mining,
title={Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration},
author={KeZheng Xiong and Haoen Xiang and Qingshan Xu and Chenglu Wen and Siqi Shen and Jonathan Li and Cheng Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OCcfKzXded}
} | Point cloud registration, a fundamental task in 3D vision, has achieved remarkable success with learning-based methods in outdoor environments. Unsupervised outdoor point cloud registration methods have recently emerged to circumvent the need for costly pose annotations. However, they fail to establish reliable optimization objectives for unsupervised training, either relying on overly strong geometric assumptions, or suffering from poor-quality pseudo-labels due to inadequate integration of low-level geometric and high-level contextual information. We have observed that in the feature space, latent new inlier correspondences tend to cluster
around respective positive anchors that summarize features of existing inliers. Motivated by this observation, we propose a novel unsupervised registration method termed INTEGER to incorporate high-level contextual information for reliable pseudo-label mining. Specifically, we propose the Feature-Geometry Coherence Mining module to dynamically adapt the teacher for each mini-batch of data during training and discover reliable pseudo-labels by considering both high-level feature representations and low-level geometric cues. Furthermore, we propose Anchor-Based Contrastive Learning to facilitate contrastive learning with anchors for a robust feature space. Lastly, we introduce a Mixed-Density Student to learn density-invariant features, addressing challenges related to density variation and low overlap in the outdoor scenario. Extensive experiments on KITTI and nuScenes datasets demonstrate that our INTEGER achieves competitive performance in terms of accuracy and generalizability. | Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration | [
"KeZheng Xiong",
"Haoen Xiang",
"Qingshan Xu",
"Chenglu Wen",
"Siqi Shen",
"Jonathan Li",
"Cheng Wang"
] | NeurIPS.cc/2024/Conference | 2411.01870 | [
"https://github.com/kezheng1204/integer"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=OCQbC0eDJJ | @inproceedings{
procaccia2024honor,
title={Honor Among Bandits: No-Regret Learning for Online Fair Division},
author={Ariel D. Procaccia and Benjamin Schiffer and Shirley Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OCQbC0eDJJ}
} | We consider the problem of online fair division of indivisible goods to players when there are a finite number of types of goods and player values are drawn from distributions with unknown means. Our goal is to maximize social welfare subject to allocating the goods fairly in expectation. When a player's value for an item is unknown at the time of allocation, we show that this problem reduces to a variant of (stochastic) multi-armed bandits, where there exists an arm for each player's value for each type of good. At each time step, we choose a distribution over arms which determines how the next item is allocated. We consider two sets of fairness constraints for this problem: envy-freeness in expectation and proportionality in expectation. Our main result is the design of an explore-then-commit algorithm that achieves $\tilde{O}(T^{2/3})$ regret while maintaining either fairness constraint. This result relies on unique properties fundamental to fair-division constraints that allow faster rates of learning, despite the restricted action space. | Honor Among Bandits: No-Regret Learning for Online Fair Division | [
"Ariel D. Procaccia",
"Benjamin Schiffer",
"Shirley Zhang"
] | NeurIPS.cc/2024/Conference | 2407.01795 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=OAjHFvrTbq | @inproceedings{
cosson2024barely,
title={Barely Random Algorithms and Collective Metrical Task Systems},
author={Romain Cosson and Laurent Massouli{\'e}},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OAjHFvrTbq}
} | We consider metrical task systems on general metric spaces with $n$ points, and show that any fully randomized algorithm can be turned into a randomized algorithm that uses only $2\log n$ random bits, and achieves the same competitive ratio up to a factor $2$. This provides the first order-optimal barely random algorithms for metrical task systems, i.e. which use a number of random bits that does not depend on the number of requests addressed to the system. We discuss implications on various aspects of online decision making such as: distributed systems, advice complexity and transaction costs, suggesting broad applicability. We put forward an equivalent view that we call collective metrical task systems where $k$ agents in a metrical task system team up, and suffer the average cost paid by each agent. Our results imply that such team can be $O(\log^2 n)$-competitive as soon as $k\geq n^2$. In comparison, a single agent is always $\Omega(n)$-competitive. | Barely Random Algorithms and Collective Metrical Task Systems | [
"Romain Cosson",
"Laurent Massoulié"
] | NeurIPS.cc/2024/Conference | 2403.11267 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=O9RZAEp34l | @inproceedings{
gopalani2024abrupt,
title={Abrupt Learning in Transformers: A Case Study on Matrix Completion},
author={Pulkit Gopalani and Ekdeep Singh Lubana and Wei Hu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O9RZAEp34l}
} | Recent analysis on the training dynamics of Transformers has unveiled an interesting characteristic: the training loss plateaus for a significant number of training steps, and then suddenly (and sharply) drops to near--optimal values. To understand this phenomenon in depth, we formulate the low-rank matrix completion problem as a masked language modeling (MLM) task, and show that it is possible to train a BERT model to solve this task to low error. Furthermore, the loss curve shows a plateau early in training followed by a sudden drop to near-optimal values, despite no changes in the training procedure or hyper-parameters. To gain interpretability insights into this sudden drop, we examine the model's predictions, attention heads, and hidden states before and after this transition. Concretely, we observe that (a) the model transitions from simply copying the masked input to accurately predicting the masked entries; (b) the attention heads transition to interpretable patterns relevant to the task; and (c) the embeddings and hidden states encode information relevant to the problem. We also analyze the training dynamics of individual model components to understand the sudden drop in loss. | Abrupt Learning in Transformers: A Case Study on Matrix Completion | [
"Pulkit Gopalani",
"Ekdeep Singh Lubana",
"Wei Hu"
] | NeurIPS.cc/2024/Conference | 2410.22244 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O97BzlN9Wh | @inproceedings{
zhang2024gder,
title={{GD}eR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning},
author={Guibin Zhang and Haonan Dong and Yuchen Zhang and Zhixun Li and Dingshuo Chen and Kai Wang and Tianlong Chen and Yuxuan Liang and Dawei Cheng and Kun Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O97BzlN9Wh}
} | Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset selection have been developed to streamline data volume by \textit{retaining}, \textit{synthesizing}, or \textit{selecting} a small yet informative subset from the full set. Among these methods, data pruning incurs the least additional training cost and offers the most practical acceleration benefits. However, it is the most vulnerable, often suffering significant performance degradation with imbalanced or biased data schema, thus raising concerns about its accuracy and reliability in on-device deployment. Therefore, there is a looming need for a new data pruning paradigm that maintains the efficiency of previous practices while ensuring balance and robustness.
Unlike the fields of computer vision and natural language processing, where mature solutions have been developed to address these issues, graph neural networks (GNNs) continue to struggle with increasingly large-scale, imbalanced, and noisy datasets, lacking a unified dataset pruning solution.
To achieve this, we introduce a novel dynamic soft-pruning method, \ourmethod, designed to update the training ``basket'' during the process using trainable prototypes. \ourmethod first constructs a well-modeled graph embedding hypersphere and then samples \textit{representative, balanced, and unbiased subsets} from this embedding space, which achieves the goal we called {\fontfamily{lmtt}\selectfont \textbf{Graph Training Debugging}}.
Extensive experiments on four datasets across three GNN backbones, demonstrate that \ourmethod (I) achieves or surpasses the performance of the full dataset with $30\%\sim50\%$ fewer training samples, (II) attains up to a $2.81\times$ lossless training speedup, and (III) outperforms state-of-the-art pruning methods in imbalanced training and noisy training scenarios by $0.3\%\sim4.3\%$ and $3.6\%\sim7.8\%$, respectively. | GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning | [
"Guibin Zhang",
"Haonan Dong",
"Yuchen Zhang",
"Zhixun Li",
"Dingshuo Chen",
"Kai Wang",
"Tianlong Chen",
"Yuxuan Liang",
"Dawei Cheng",
"Kun Wang"
] | NeurIPS.cc/2024/Conference | 2410.13761 | [
"https://github.com/ins1stenc3/gder"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O8yHsRLwPl | @inproceedings{
tyurin2024shadowheart,
title={Shadowheart {SGD}: Distributed Asynchronous {SGD} with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity},
author={Alexander Tyurin and Marta Pozzi and Ivan Ilin and Peter Richt{\'a}rik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O8yHsRLwPl}
} | We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored, and the computation and communication times are potentially different for all workers. Using an unbiassed compression technique, we develop a new method—Shadowheart SGD—that provably improves the time complexities of all previous centralized methods. Moreover, we show that the time complexity of Shadowheart SGD is optimal in the family of centralized methods with compressed communication. We also consider the bidirectional setup, where broadcasting from the server to the workers is non-negligible, and develop a corresponding method. | Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity | [
"Alexander Tyurin",
"Marta Pozzi",
"Ivan Ilin",
"Peter Richtárik"
] | NeurIPS.cc/2024/Conference | 2402.04785 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O7IN4nsaIO | @inproceedings{
huang2024achieving,
title={Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes},
author={Yan Huang and Xiang Li and Yipeng Shen and Niao He and Jinming Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O7IN4nsaIO}
} | In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to employ an adaptive stepsize tracking protocol involving the transmission of two extra (scalar) variables. This protocol ensures the consistency among stepsizes of nodes, eliminating the steady-state error due to the lack of coordination of stepsizes among nodes that commonly exists in vanilla distributed adaptive methods, and thus guarantees exact convergence. For nonconvex-strongly-concave distributed minimax problems, we characterize the specific transient times that ensure time-scale separation of stepsizes and quasi-independence of networks, leading to a near-optimal convergence rate of $\tilde{\mathcal{O}} \left( \epsilon ^{-\left( 4+\delta \right)} \right)$ for any small $\delta > 0$, matching that of the centralized counterpart. To our best knowledge, D-AdaST is the *first* distributed adaptive method achieving near-optimal convergence without knowing any problem-dependent parameters for nonconvex minimax problems. Extensive experiments are conducted to validate our theoretical results. | Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes | [
"Yan Huang",
"Xiang Li",
"Yipeng Shen",
"Niao He",
"Jinming Xu"
] | NeurIPS.cc/2024/Conference | 2406.02939 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O5XbOoi0x3 | @inproceedings{
ren2024hypersd,
title={Hyper-{SD}: Trajectory Segmented Consistency Model for Efficient Image Synthesis},
author={Yuxi Ren and Xin Xia and Yanzuo Lu and Jiacheng Zhang and Jie Wu and Pan Xie and XING WANG and Xuefeng Xiao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O5XbOoi0x3}
} | Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs). Current distillation techniques often dichotomize into two distinct aspects: i) ODE Trajectory Preservation; and ii) ODE Trajectory Reformulation. However, these approaches suffer from severe performance degradation or domain shifts. To address these limitations, we propose Hyper-SD, a novel framework that synergistically amalgamates the advantages of ODE Trajectory Preservation and Reformulation, while maintaining near-lossless performance during step compression. Firstly, we introduce Trajectory Segmented Consistency Distillation to progressively perform consistent distillation within pre-defined time-step segments, which facilitates the preservation of the original ODE trajectory from a higher-order perspective. Secondly, we incorporate human feedback learning to boost the performance of the model in a low-step regime and mitigate the performance loss incurred by the distillation process. Thirdly, we integrate score distillation to further improve the low-step generation capability of the model and offer the first attempt to leverage a unified LoRA to support the inference process at all steps. Extensive experiments and user studies demonstrate that Hyper-SD achieves SOTA performance from 1 to 8 inference steps for both SDXL and SD1.5. For example, Hyper-SDXL surpasses SDXL-Lightning by +0.68 in CLIP Score and +0.51 in Aes Score in the 1-step inference. | Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis | [
"Yuxi Ren",
"Xin Xia",
"Yanzuo Lu",
"Jiacheng Zhang",
"Jie Wu",
"Pan Xie",
"XING WANG",
"Xuefeng Xiao"
] | NeurIPS.cc/2024/Conference | 2404.13686 | [
""
] | https://huggingface.co/papers/2404.13686 | 5 | 27 | 2 | 8 | [
"ByteDance/Hyper-SD",
"ProfessorFT/AIPG_RED",
"rootonchair/tscd_juggernaut_final"
] | [] | [
"ByteDance/Hyper-FLUX-8Steps-LoRA",
"ByteDance/Hyper-SDXL-1Step-T2I",
"ByteDance/Hyper-SD15-Scribble",
"multimodalart/one-step-comparison",
"multimodalart/flux-outpainting",
"John6666/DiffuseCraftMod",
"r3gm/DiffuseCraft",
"John6666/votepurchase-multiple-model",
"radames/InstantStyle-Hyper-SD",
"doevent/FLUX.1-merged",
"fffiloni/ReNO",
"multimodalart/low-step-flux-comparison",
"eienmojiki/AnyDiffuse",
"radames/InstantStyle-Hyper-SDXL",
"tuan2308/DiffuseCraft",
"mantrakp/aai",
"zerhero/DiffuseCraft",
"John6666/sdxl-to-diffusers-v2",
"HRJ360/AI-STORYTELLER",
"fantos/flx8lora",
"fcyai/Hyper-FLUX-8Steps-LoRA",
"Menyu/DiffuseCraftMod",
"John6666/sdxl-to-diffusers-v2p",
"alsaeth/ByteDance-Hyper-SD",
"John6666/safetensors_to_diffusers",
"charbel-malo/flux-loras",
"EVA787797/kiii44545454",
"Nando35/DiffuseCraft",
"John6666/sdxl-to-diffusers-v2-cliptest",
"K00B404/Hyper-SDXL-1Step-T2I-cpu",
"shivguddadmath/Hyper-SDXL",
"Falln87/Hyper-SD15-Scribble",
"FallnAI/HyperSD15-Scribble",
"mba07m/Hackathon3D",
"Nymbo/sdxl-to-diffusers-v2",
"banan1233op/hypersd-sdxl",
"Iwaku-Real/Hyper-SDXL-1Step-T2I",
"xbbd/ByteDance-Hyper-SD",
"HuggingFaceSupport/ByteDance-Hyper-SD",
"rencent/ByteDance-Hyper-SD",
"Raumkommander/Hyper-FLUX-8Steps-LoRA",
"marsyao/Hyper-FLUX-8Steps-LoRA",
"johnstonkaren314/ByteDance-Hyper-SD",
"AnonDev/ByteDance-Hyper-SD",
"Naranko/ByteDance-Hyper-SD",
"bruvvyluvvy/Hyper-FLUX-8Steps-LoRA",
"Afrinetwork/ig",
"somukandula/ByteDance-Hyper-SD",
"Aditya2034/abc21",
"Larm/ByteDance-Hyper-SD",
"a2post/Hyper-FLUX-8Steps-LoRA",
"vijaykumar8560/vijayimage",
"K00B404/Hyper-FLUX-8Steps-LoRA_CPU",
"nightfury/Hyper-FLUX-8Steps-LoRA",
"Evansville/ByteDance-Hyper-SD",
"Fili2a2/DIGITAL-PROSPECTIVE-Hyper-SD",
"Afrinetwork/ig1",
"GQ123QWE/ByteDance-Hyper-SD",
"Vivawaves/Hyper-FLUX-8Steps-LoRA",
"JeCabrera/AI-STORYTELLER2",
"Funpee/Hyper-FLUX-8Steps-LoRA",
"callzz/sdxl-to-diffusers-v2",
"Nymbo/flux-outpainting",
"JohnyLahente/flux-outpainting",
"huanhoang/flux-outpainting",
"GarryB/flux-outpainting-rim",
"GarryB/flux-outpainting-rim2"
] | [
"ByteDance/Hyper-SD",
"ProfessorFT/AIPG_RED",
"rootonchair/tscd_juggernaut_final"
] | [] | [
"ByteDance/Hyper-FLUX-8Steps-LoRA",
"ByteDance/Hyper-SDXL-1Step-T2I",
"ByteDance/Hyper-SD15-Scribble",
"multimodalart/one-step-comparison",
"multimodalart/flux-outpainting",
"John6666/DiffuseCraftMod",
"r3gm/DiffuseCraft",
"John6666/votepurchase-multiple-model",
"radames/InstantStyle-Hyper-SD",
"doevent/FLUX.1-merged",
"fffiloni/ReNO",
"multimodalart/low-step-flux-comparison",
"eienmojiki/AnyDiffuse",
"radames/InstantStyle-Hyper-SDXL",
"tuan2308/DiffuseCraft",
"mantrakp/aai",
"zerhero/DiffuseCraft",
"John6666/sdxl-to-diffusers-v2",
"HRJ360/AI-STORYTELLER",
"fantos/flx8lora",
"fcyai/Hyper-FLUX-8Steps-LoRA",
"Menyu/DiffuseCraftMod",
"John6666/sdxl-to-diffusers-v2p",
"alsaeth/ByteDance-Hyper-SD",
"John6666/safetensors_to_diffusers",
"charbel-malo/flux-loras",
"EVA787797/kiii44545454",
"Nando35/DiffuseCraft",
"John6666/sdxl-to-diffusers-v2-cliptest",
"K00B404/Hyper-SDXL-1Step-T2I-cpu",
"shivguddadmath/Hyper-SDXL",
"Falln87/Hyper-SD15-Scribble",
"FallnAI/HyperSD15-Scribble",
"mba07m/Hackathon3D",
"Nymbo/sdxl-to-diffusers-v2",
"banan1233op/hypersd-sdxl",
"Iwaku-Real/Hyper-SDXL-1Step-T2I",
"xbbd/ByteDance-Hyper-SD",
"HuggingFaceSupport/ByteDance-Hyper-SD",
"rencent/ByteDance-Hyper-SD",
"Raumkommander/Hyper-FLUX-8Steps-LoRA",
"marsyao/Hyper-FLUX-8Steps-LoRA",
"johnstonkaren314/ByteDance-Hyper-SD",
"AnonDev/ByteDance-Hyper-SD",
"Naranko/ByteDance-Hyper-SD",
"bruvvyluvvy/Hyper-FLUX-8Steps-LoRA",
"Afrinetwork/ig",
"somukandula/ByteDance-Hyper-SD",
"Aditya2034/abc21",
"Larm/ByteDance-Hyper-SD",
"a2post/Hyper-FLUX-8Steps-LoRA",
"vijaykumar8560/vijayimage",
"K00B404/Hyper-FLUX-8Steps-LoRA_CPU",
"nightfury/Hyper-FLUX-8Steps-LoRA",
"Evansville/ByteDance-Hyper-SD",
"Fili2a2/DIGITAL-PROSPECTIVE-Hyper-SD",
"Afrinetwork/ig1",
"GQ123QWE/ByteDance-Hyper-SD",
"Vivawaves/Hyper-FLUX-8Steps-LoRA",
"JeCabrera/AI-STORYTELLER2",
"Funpee/Hyper-FLUX-8Steps-LoRA",
"callzz/sdxl-to-diffusers-v2",
"Nymbo/flux-outpainting",
"JohnyLahente/flux-outpainting",
"huanhoang/flux-outpainting",
"GarryB/flux-outpainting-rim",
"GarryB/flux-outpainting-rim2"
] | 1 | poster |
null | https://openreview.net/forum?id=O4RCFjVUBJ | @inproceedings{
dong2024how,
title={How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?},
author={Jiahua Dong and Wenqi Liang and Hongliu Li and Duzhen Zhang and Meng Cao and Henghui Ding and Salman Khan and Fahad Khan},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O4RCFjVUBJ}
} | Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts. However, most existing CDMs unreasonably assume that personalized concepts are fixed and cannot change over time. Moreover, they heavily suffer from catastrophic forgetting and concept neglect on old personalized concepts when continually learning a series of new concepts. To address these challenges, we propose a novel Concept-Incremental text-to-image Diffusion Model (CIDM), which can resolve catastrophic forgetting and concept neglect to learn new customization tasks in a concept-incremental manner. Specifically, to surmount the catastrophic forgetting of old concepts, we develop a concept consolidation loss and an elastic weight aggregation module. They can explore task-specific and task-shared knowledge during training, and aggregate all low-rank weights of old concepts based on their contributions during inference. Moreover, in order to address concept neglect, we devise a context-controllable synthesis strategy that leverages expressive region features and noise estimation to control the contexts of generated images according to user conditions. Experiments validate that our CIDM surpasses existing custom diffusion models. The source codes are available at https://github.com/JiahuaDong/CIFC. | How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization? | [
"Jiahua Dong",
"Wenqi Liang",
"Hongliu Li",
"Duzhen Zhang",
"Meng Cao",
"Henghui Ding",
"Salman Khan",
"Fahad Khan"
] | NeurIPS.cc/2024/Conference | 2410.17594 | [
"https://github.com/jiahuadong/cifc"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O3nPufVaee | @inproceedings{
quan2024pseudosiamese,
title={Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising},
author={Yuhui Quan and Tianxiang Zheng and Hui Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O3nPufVaee}
} | Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the transformer’s capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel–a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a half-plane grid for self-attention, creating a sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impacts
from the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-image-free methods. This combination of blind-spot and transformer techniques provides a natural synergy for tackling real-world image denoising challenges. | Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising | [
"Yuhui Quan",
"Tianxiang Zheng",
"Hui Ji"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=O2UwxfhY1P | @inproceedings{
huang2024on,
title={On the Comparison between Multi-modal and Single-modal Contrastive Learning},
author={Wei Huang and Andi Han and Yongqiang Chen and Yuan Cao and zhiqiang xu and Taiji Suzuki},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O2UwxfhY1P}
} | Multi-modal contrastive learning with language supervision has presented a paradigm shift in modern machine learning. By pre-training on a web-scale dataset, multi-modal contrastive learning can learn high-quality representations that exhibit impressive robustness and transferability. Despite its empirical success, the theoretical understanding is still in its infancy, especially regarding its comparison with single-modal contrastive learning. In this work, we introduce a feature learning theory framework that provides a theoretical foundation for understanding the differences between multi-modal and single-modal contrastive learning. Based on a data generation model consisting of signal and noise, our analysis is performed on a ReLU network trained with the InfoMax objective function. Through a trajectory-based optimization analysis and generalization characterization on downstream tasks, we identify the critical factor, which is the signal-to-noise ratio (SNR), that impacts the generalizability in downstream tasks of both multi-modal and single-modal contrastive learning. Through the cooperation between the two modalities, multi-modal learning can achieve better feature learning, leading to improvements in performance in downstream tasks compared to single-modal learning. Our analysis provides a unified framework that can characterize the optimization and generalization of both single-modal and multi-modal contrastive learning. Empirical experiments on both synthetic and real-world datasets further consolidate our theoretical findings. | On the Comparison between Multi-modal and Single-modal Contrastive Learning | [
"Wei Huang",
"Andi Han",
"Yongqiang Chen",
"Yuan Cao",
"zhiqiang xu",
"Taiji Suzuki"
] | NeurIPS.cc/2024/Conference | 2411.02837 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O23XfTnhWR | @inproceedings{
russold2024graphcode,
title={Graphcode: Learning from multiparameter persistent homology using graph neural networks},
author={Florian Russold and Michael Kerber},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O23XfTnhWR}
} | We introduce graphcodes, a novel multi-scale summary of the topological properties of a dataset that is based on the well-established theory of persistent homology. Graphcodes handle datasets that are filtered along two real-valued scale parameters. Such multi-parameter topological summaries are usually based on complicated theoretical foundations and difficult to compute; in contrast, graphcodes yield an informative and interpretable summary and can be computed as efficient as one-parameter summaries. Moreover, a graphcode is simply an embedded graph and can therefore be readily integrated in machine learning pipelines using graph neural networks. We describe such a pipeline and demonstrate that graphcodes achieve better classification accuracy than state-of-the-art approaches on various datasets. | Graphcode: Learning from multiparameter persistent homology using graph neural networks | [
"Florian Russold",
"Michael Kerber"
] | NeurIPS.cc/2024/Conference | 2405.14302 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O1fp9nVraj | @inproceedings{
kenton2024on,
title={On scalable oversight with weak {LLM}s judging strong {LLM}s},
author={Zachary Kenton and Noah Yamamoto Siegel and Janos Kramar and Jonah Brown-Cohen and Samuel Albanie and Jannis Bulian and Rishabh Agarwal and David Lindner and Yunhao Tang and Noah Goodman and Rohin Shah},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O1fp9nVraj}
} | Scalable oversight protocols aim to enable humans to accurately supervise superhuman AI.
In this paper we study debate, where two AI's compete to convince a judge; consultancy,
where a single AI tries to convince a judge that asks questions;
and compare to a baseline of direct question-answering, where the judge just answers outright without the AI.
We use large language models (LLMs) as both AI agents and as stand-ins for human judges, taking the judge models to be weaker than agent models.
We benchmark on a diverse range of asymmetries between judges and agents, extending previous work on a single extractive QA task with information asymmetry, to also include mathematics, coding, logic and multimodal reasoning asymmetries.
We find that debate outperforms consultancy across all tasks when the consultant is randomly assigned to argue for the correct/incorrect answer. Comparing debate to direct question answering, the results depend on the type of task: in extractive QA tasks with information asymmetry debate outperforms direct question answering, but in other tasks without information asymmetry the results are mixed.
Previous work assigned debaters/consultants an answer to argue for. When we allow them to instead choose which answer to argue for, we find judges are less frequently convinced by the wrong answer in debate than in consultancy.
Further, we find that stronger debater models increase judge accuracy, though more modestly than in previous studies. | On scalable oversight with weak LLMs judging strong LLMs | [
"Zachary Kenton",
"Noah Yamamoto Siegel",
"Janos Kramar",
"Jonah Brown-Cohen",
"Samuel Albanie",
"Jannis Bulian",
"Rishabh Agarwal",
"David Lindner",
"Yunhao Tang",
"Noah Goodman",
"Rohin Shah"
] | NeurIPS.cc/2024/Conference | 2407.04622 | [
""
] | https://huggingface.co/papers/2407.04622 | 8 | 11 | 1 | 11 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=O0nBMRlkc8 | @inproceedings{
wang2024mobileagentv,
title={Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration},
author={Junyang Wang and Haiyang Xu and Haitao Jia and Xi Zhang and Ming Yan and Weizhou Shen and Ji Zhang and Fei Huang and Jitao Sang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O0nBMRlkc8}
} | Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their training data, lack the capability to function effectively as operation assistants. Instead, MLLM-based agents, which enhance capabilities through tool invocation, are gradually being applied to this scenario. However, the two major navigation challenges in mobile device operation tasks — task progress navigation and focus content navigation — are difficult to effectively solve under the single-agent architecture of existing work. This is due to the overly long token sequences and the interleaved text-image data format, which limit performance. To address these navigation challenges effectively, we propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. The planning agent condenses lengthy, interleaved image-text history operations and screens summaries into a pure-text task progress, which is then passed on to the decision agent. This reduction in context length makes it easier for decision agent to navigate the task progress. To retain focus content, we design a memory unit that updates with task progress by decision agent. Additionally, to correct erroneous operations, the reflection agent observes the outcomes of each operation and handles any mistake accordingly. Experimental results indicate that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture of Mobile-Agent. The code is open-sourced at https://github.com/X-PLUG/MobileAgent. | Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration | [
"Junyang Wang",
"Haiyang Xu",
"Haitao Jia",
"Xi Zhang",
"Ming Yan",
"Weizhou Shen",
"Ji Zhang",
"Fei Huang",
"Jitao Sang"
] | NeurIPS.cc/2024/Conference | 2406.01014 | [
"https://github.com/x-plug/mobileagent"
] | https://huggingface.co/papers/2406.01014 | 5 | 31 | 2 | 9 | [] | [] | [
"junyangwang0410/Mobile-Agent"
] | [] | [] | [
"junyangwang0410/Mobile-Agent"
] | 1 | poster |
null | https://openreview.net/forum?id=Nzfg1LXTdS | @inproceedings{
liang2024how,
title={How Diffusion Models Learn to Factorize and Compose},
author={Qiyao Liang and Ziming Liu and Mitchell Ostrow and Ila R Fiete},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Nzfg1LXTdS}
} | Diffusion models are capable of generating photo-realistic images that combine elements which do not appear together in natural images, demonstrating their ability to compositionally generalize. Nonetheless, the precise mechanism of compositionality and how it is acquired through training remains elusive. Here, we consider a highly reduced setting to examine whether diffusion models learn semantically meaningful and fully factorized representations of composable features. We performed extensive controlled experiments on conditional DDPMs trained to generate various forms of 2D Gaussian data. We demonstrate that the models learn factorized, semi-continuous manifold representations that are orthogonal in underlying continuous latent features of independent variations but are not aligned for different values of the same feature. With such representations, models demonstrate superior compositionality but have limited ability to interpolate over unseen values of a given feature. Our experimental results further demonstrate that diffusion models can attain compositionality with a small amount of compositional examples, suggesting a novel way to train DDPMs. Finally, we connect manifold formation in diffusion models to percolation theory in physics, thereby offering insights into the sudden onset of factorized representation learning. Our thorough toy experiments thus contribute a deeper understanding of how diffusion models capture compositional structure in data, paving the way for future research aimed at enhancing factorization and compositional generalization in generative models for real-world applications. | How Diffusion Models Learn to Factorize and Compose | [
"Qiyao Liang",
"Ziming Liu",
"Mitchell Ostrow",
"Ila R Fiete"
] | NeurIPS.cc/2024/Conference | 2408.13256 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Nycj81Z692 | @inproceedings{
ning2024urbankgent,
title={Urban{KG}ent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction},
author={Yansong Ning and Hao Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Nycj81Z692}
} | Urban knowledge graph has recently worked as an emerging building block to distill critical knowledge from multi-sourced urban data for diverse urban application scenarios. Despite its promising benefits, urban knowledge graph construction (UrbanKGC) still heavily relies on manual effort, hindering its potential advancement. This paper presents UrbanKGent, a unified large language model agent framework, for urban knowledge graph construction. Specifically, we first construct the knowledgeable instruction set for UrbanKGC tasks (such as relational triplet extraction and knowledge graph completion) via heterogeneity-aware and geospatial-infused instruction generation. Moreover, we propose a tool-augmented iterative trajectory refinement module to enhance and refine the trajectories distilled from GPT-4. Through hybrid instruction fine-tuning with augmented trajectories on Llama 2 and Llama 3 family, we obtain UrbanKGC agent family, consisting of UrbanKGent-7/8/13B version. We perform a comprehensive evaluation on two real-world datasets using both human and GPT-4 self-evaluation. The experimental results demonstrate that UrbanKGent family can not only significantly outperform 31 baselines in UrbanKGC tasks, but also surpass the state-of-the-art LLM, GPT-4, by more than 10% with approximately 20 times lower cost. Compared with the existing benchmark, the UrbanKGent family could help construct an UrbanKG with hundreds of times richer relationships using only one-fifth of the data. Our data and code are available at https://github.com/usail-hkust/UrbanKGent. | UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction | [
"Yansong Ning",
"Hao Liu"
] | NeurIPS.cc/2024/Conference | 2402.06861 | [
"https://github.com/usail-hkust/urbankgent"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=NwiFLtWGEg | @inproceedings{
luo2024reinforcement,
title={Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control},
author={Jinzhu Luo and Dingyang Chen and Qi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NwiFLtWGEg}
} | Data augmentation creates new data points by transforming the original ones for an reinforcement learning (RL) agent to learn from, which has been shown to be effective for the objective of improving data efficiency of RL for continuous control. Prior work towards this objective has been largely restricted to perturbation-based data augmentation where new data points are created by perturbing the original ones,
which has been impressively effective for tasks where the RL agent observe control states as images with perturbations including random cropping, shifting, etc. This work focuses on state-based control, where the RL agent can directly observe raw kinematic and task features, and considers an alternative data augmentation applied to these features based on Euclidean symmetries under transformations like rotations. We show that the default state features used in exiting benchmark tasks that are based on joint configurations are not amenable to Euclidean transformations. We therefore advocate using state features based on configurations of the limbs (i.e., rigid bodies connected by joints) that instead provides rich augmented data under Euclidean transformations. With minimal hyperparameter tuning, we show this new Euclidean data augmentation strategy significantly improve both data efficiency and asymptotic performance of RL on a wide range of continuous control tasks. | Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control | [
"Jinzhu Luo",
"Dingyang Chen",
"Qi Zhang"
] | NeurIPS.cc/2024/Conference | 2410.12983 | [
"https://github.com/jinzhuluo/euclideanda"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Nv0Vvz588D | @inproceedings{
silva2024streaming,
title={Streaming Bayes {GF}lowNets},
author={Tiago Silva and Daniel Augusto de Souza and Diego Mesquita},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Nv0Vvz588D}
} | Bayes' rule naturally allows for inference refinement in a streaming fashion, without the need to recompute posteriors from scratch whenever new data arrives. In principle, Bayesian streaming is straightforward: we update our prior with the available data and use the resulting posterior as a prior when processing the next data chunk. In practice, however, this recipe entails i) approximating an intractable posterior at each time step; and ii) encapsulating results appropriately to allow for posterior propagation. For continuous state spaces, variational inference (VI) is particularly convenient due to its scalability and the tractability of variational posteriors, For discrete state spaces, however, state-of-the-art VI results in analytically intractable approximations that are ill-suited for streaming settings. To enable streaming Bayesian inference over discrete parameter spaces, we propose streaming Bayes GFlowNets (abbreviated as SB-GFlowNets) by leveraging the recently proposed GFlowNets --- a powerful class of amortized samplers for discrete compositional objects. Notably, SB-GFlowNet approximates the initial posterior using a standard GFlowNet and subsequently updates it using a tailored procedure that requires only the newly observed data. Our case studies in linear preference learning and phylogenetic inference showcase the effectiveness of SB-GFlowNets in sampling from an unnormalized posterior in a streaming setting. As expected, we also observe that SB-GFlowNets is significantly faster than repeatedly training a GFlowNet from scratch to sample from the full posterior. | Streaming Bayes GFlowNets | [
"Tiago Silva",
"Daniel Augusto de Souza",
"Diego Mesquita"
] | NeurIPS.cc/2024/Conference | 2411.05899 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=NtNTfRTjE8 | @inproceedings{
zheng2024breaking,
title={Breaking Semantic Artifacts for Generalized {AI}-generated Image Detection},
author={Chende Zheng and Chenhao Lin and Zhengyu Zhao and Hang Wang and Xu Guo and Shuai Liu and Chao Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NtNTfRTjE8}
} | With the continuous evolution of AI-generated images, the generalized detection of them has become a crucial aspect of AI security.
Existing detectors have focused on cross-generator generalization, while it remains unexplored whether these detectors can generalize across different image scenes, e.g., images from different datasets with different semantics. In this paper, we reveal that existing detectors suffer from substantial Accuracy drops in such cross-scene generalization. In particular, we attribute their failures to ''semantic artifacts'' in both real and generated images, to which detectors may overfit. To break such ''semantic artifacts'', we propose a simple yet effective approach based on conducting an image patch shuffle and then training an end-to-end patch-based classifier. We conduct a comprehensive open-world evaluation on 31 test sets, covering 7 Generative Adversarial Networks, 18 (variants of) Diffusion Models, and another 6 CNN-based generative models. The results demonstrate that our approach outperforms previous approaches by 2.08\% (absolute) on average regarding cross-scene detection Accuracy. We also notice the superiority of our approach in open-world generalization, with an average Accuracy improvement of 10.59\% (absolute) across all test sets. | Breaking Semantic Artifacts for Generalized AI-generated Image Detection | [
"Chende Zheng",
"Chenhao Lin",
"Zhengyu Zhao",
"Hang Wang",
"Xu Guo",
"Shuai Liu",
"Chao Shen"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NsxthTVpqA | @inproceedings{
xiao2024seeing,
title={Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment},
author={Xin Xiao and Bohong Wu and Jiacong Wang and Chunyuan Li and zhou Xun and Haoyuan Guo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NsxthTVpqA}
} | Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner. Despite being simple and effective, this method results in sub-optimal cross-modal alignment by over-emphasizing the text tokens that are less correlated with or even contradictory with the input images. In this paper, we advocate for distinct contributions for each text token based on its visual correlation. Specifically, we present by contrasting image inputs, the difference in prediction logits on each text token provides strong guidance of visual correlation. We therefore introduce Contrastive Alignment (CAL), a simple yet effective re-weighting strategy that prioritizes training visually correlated tokens. Our experimental results demonstrate that CAL consistently improves different types of VLMs across different resolutions and model sizes on various benchmark datasets. Importantly, our method incurs minimal additional computational overhead, rendering it highly efficient compared to alternative data scaling strategies. | Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment | [
"Xin Xiao",
"Bohong Wu",
"Jiacong Wang",
"Chunyuan Li",
"zhou Xun",
"Haoyuan Guo"
] | NeurIPS.cc/2024/Conference | 2405.17871 | [
"https://github.com/foundation-multimodal-models/cal"
] | https://huggingface.co/papers/2405.17871 | 2 | 1 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=NsqxN9iOJ7 | @inproceedings{
zhai2024motion,
title={Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation},
author={Yuanhao Zhai and Kevin Lin and Zhengyuan Yang and Linjie Li and Jianfeng Wang and Chung-Ching Lin and David Doermann and Junsong Yuan and Lijuan Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NsqxN9iOJ7}
} | Image diffusion distillation achieves high-fidelity generation with very few sampling steps. However, directly applying these techniques to video models results in unsatisfied frame quality. This issue arises from the limited frame appearance quality in public video datasets, affecting the performance of both teacher and student video diffusion models. Our study aims to improve video diffusion distillation and meanwhile enabling the student model to improve frame appearance using the abundant high-quality image data. To this end, we propose motion consistency models (MCM), a single-stage video diffusion distillation method that disentangles motion and appearance learning. Specifically, MCM involves a video consistency model that distills motion from the video teacher model, and an image discriminator that boosts frame appearance to match high-quality image data. However, directly combining these components leads to two significant challenges: a conflict in frame learning objectives, where video distillation learns from low-quality video frames while the image discriminator targets high-quality images, and training-inference discrepancies due to the differing quality of video samples used during training and inference. To address these challenges, we introduce disentangled motion distillation and mixed trajectory distillation. The former applies the distillation objective solely to the motion representation, while the latter mitigates training-inference discrepancies by mixing distillation trajectories from both the low- and high-quality video domains. Extensive experiments show that our MCM achieves state-of-the-art video diffusion distillation performance. Additionally, our method can enhance frame quality in video diffusion models, producing frames with high aesthetic value or specific styles. | Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation | [
"Yuanhao Zhai",
"Kevin Lin",
"Zhengyuan Yang",
"Linjie Li",
"Jianfeng Wang",
"Chung-Ching Lin",
"David Doermann",
"Junsong Yuan",
"Lijuan Wang"
] | NeurIPS.cc/2024/Conference | 2406.06890 | [
"https://github.com/yhZhai/mcm"
] | https://huggingface.co/papers/2406.06890 | 1 | 1 | 0 | 9 | [
"yhzhai/mcm"
] | [] | [
"yhzhai/mcm"
] | [
"yhzhai/mcm"
] | [] | [
"yhzhai/mcm"
] | 1 | poster |
null | https://openreview.net/forum?id=Ns0LQokxa5 | @inproceedings{
jain2024gaussiancut,
title={GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting},
author={Umangi Jain and Ashkan Mirzaei and Igor Gilitschenski},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ns0LQokxa5}
} | We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented by interacting with a single view. It accepts intuitive user input, such as point clicks, coarse scribbles, or text. Using 3D Gaussian Splatting (3DGS) as the underlying scene representation simplifies the extraction of objects of interest which are considered to be a subset of the scene's Gaussians. Our key idea is to represent the scene as a graph and use the graph-cut algorithm to minimize an energy function to effectively partition the Gaussians into foreground and background. To achieve this, we construct a graph based on scene Gaussians and devise a segmentation-aligned energy function on the graph to combine user inputs with scene properties. To obtain an initial coarse segmentation, we leverage 2D image/video segmentation models and further refine these coarse estimates using our graph construction. Our empirical evaluations show the adaptability of GaussianCut across a diverse set of scenes. GaussianCut achieves competitive performance with state-of-the-art approaches for 3D segmentation without requiring any additional segmentation-aware training | GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting | [
"Umangi Jain",
"Ashkan Mirzaei",
"Igor Gilitschenski"
] | NeurIPS.cc/2024/Conference | 2411.07555 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=NrwASKGm7A | @inproceedings{
gu2024anahv,
title={{ANAH}-v2: Scaling Analytical Hallucination Annotation of Large Language Models},
author={Yuzhe Gu and Ziwei Ji and Wenwei Zhang and Chengqi Lyu and Dahua Lin and Kai Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NrwASKGm7A}
} | Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks across various domains and wide applications. Current hallucination detection and mitigation datasets are limited in domain and size, which struggle to scale due to prohibitive labor costs and insufficient reliability of existing hallucination annotators. To facilitate the scalable oversight of LLM hallucinations, this paper introduces an iterative self-training framework that simultaneously and progressively scales up the annotation dataset and improves the accuracy of the annotator. Based on the Expectation Maximization algorithm, in each iteration, the framework first applies an automatic hallucination annotation pipeline for a scaled dataset and then trains a more accurate annotator on the dataset. This new annotator is adopted in the annotation pipeline for the next iteration. Extensive experimental results demonstrate that the finally obtained hallucination annotator with only 7B parameters surpasses GPT-4 and obtains new state-of-the-art hallucination detection results on HaluEval and HalluQA by zero-shot inference. Such an annotator can not only evaluate the hallucination levels of various LLMs on the large-scale dataset but also help to mitigate the hallucination of LLMs generations, with the Natural Language Inference metric increasing from 25% to 37% on HaluEval. | ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models | [
"Yuzhe Gu",
"Ziwei Ji",
"Wenwei Zhang",
"Chengqi Lyu",
"Dahua Lin",
"Kai Chen"
] | NeurIPS.cc/2024/Conference | 2407.04693 | [
"https://github.com/open-compass/anah"
] | https://huggingface.co/papers/2407.04693 | 4 | 1 | 1 | 6 | [
"opencompass/anah-v2"
] | [] | [] | [
"opencompass/anah-v2"
] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=Nq8enbbaP2 | @inproceedings{
huang2024occupancybased,
title={Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality},
author={Audrey Huang and Nan Jiang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Nq8enbbaP2}
} | Occupancy functions play an instrumental role in reinforcement learning (RL) for guiding exploration, handling distribution shift, and optimizing general objectives beyond the expected return. Yet, computationally efficient policy optimization methods that use (only) occupancy functions are virtually non-existent. In this paper, we establish the theoretical foundations of model-free policy gradient (PG) methods that compute the gradient through the occupancy for both online and offline RL, without modeling value functions. Our algorithms reduce gradient estimation to squared-loss regression and are computationally oracle-efficient. We characterize the sample complexities of both local and global convergence, accounting for both finite-sample estimation error and the roles of exploration (online) and data coverage (offline). Occupancy-based PG naturally handles arbitrary offline data distributions, and, with one-line algorithmic changes, can be adapted to optimize any differentiable objective functional. | Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality | [
"Audrey Huang",
"Nan Jiang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NnoAj91HZX | @inproceedings{
oki2024noregret,
title={No-Regret M\$\{\}{\textasciicircum}\{{\textbackslash}natural\}\$-Concave Function Maximization: Stochastic Bandit Algorithms and {NP}-Hardness of Adversarial Full-Information Setting},
author={Taihei Oki and Shinsaku Sakaue},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NnoAj91HZX}
} | M${}^{\natural}$-concave functions, a.k.a. gross substitute valuation functions, play a fundamental role in many fields, including discrete mathematics and economics. In practice, perfect knowledge of M${}^{\natural}$-concave functions is often unavailable a priori, and we can optimize them only interactively based on some feedback. Motivated by such situations, we study online M${}^{\natural}$-concave function maximization problems, which are interactive versions of the problem studied by Murota and Shioura (1999). For the stochastic bandit setting, we present $O(T^{-1/2})$-simple regret and $O(T^{2/3})$-regret algorithms under $T$ times access to unbiased noisy value oracles of M${}^{\natural}$-concave functions. A key to proving these results is the robustness of the greedy algorithm to local errors in M${}^{\natural}$-concave function maximization, which is one of our main technical results. While we obtain those positive results for the stochastic setting, another main result of our work is an impossibility in the adversarial setting. We prove that, even with full-information feedback, no algorithms that run in polynomial time per round can achieve $O(T^{1-c})$ regret for any constant $c > 0$ unless $\mathsf{P} = \mathsf{NP}$. Our proof is based on a reduction from the matroid intersection problem for three matroids, which would be a novel idea in the context of online learning. | No-Regret M^♮-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting | [
"Taihei Oki",
"Shinsaku Sakaue"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NnAi0L5H8J | @inproceedings{
tang2024multiinstance,
title={Multi-Instance Partial-Label Learning with Margin Adjustment},
author={Wei Tang and Yin-Fang Yang and Zhaofei Wang and Weijia Zhang and Min-Ling Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NnAi0L5H8J}
} | Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution
loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms. | Multi-Instance Partial-Label Learning with Margin Adjustment | [
"Wei Tang",
"Yin-Fang Yang",
"Zhaofei Wang",
"Weijia Zhang",
"Min-Ling Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Nmmiyjw7Xg | @inproceedings{
tang2024safe,
title={Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport},
author={Zihao Tang and Yixuan Qiu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Nmmiyjw7Xg}
} | Computational optimal transport (OT) has received massive interests in the machine learning community, and great advances have been gained in the direction of entropic-regularized OT. The Sinkhorn algorithm, as well as its many improved versions, has become the *de facto* solution to large-scale OT problems. However, most of the existing methods behave like first-order methods, which typically require a large number of iterations to converge. More recently, Newton-type methods using sparsified Hessian matrices have demonstrated promising results on OT computation, but there still remain a lot of unresolved open questions. In this article, we make major new progresses towards this direction: first, we propose a novel Hessian sparsification scheme that promises a strict control of the approximation error; second, based on this sparsification scheme, we develop a *safe* Newton-type method that is guaranteed to avoid singularity in computing the search directions; third, the developed algorithm has a clear implementation for practical use, avoiding most hyperparameter tuning; and remarkably, we provide rigorous global and local convergence analysis of the proposed algorithm, which is lacking in the prior literature. Various numerical experiments are conducted to demonstrate the effectiveness of the proposed algorithm in solving large-scale OT problems. | Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport | [
"Zihao Tang",
"Yixuan Qiu"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NmlnmLYMZ4 | @inproceedings{
sundaram2024when,
title={When does perceptual alignment benefit vision representations?},
author={Shobhita Sundaram and Stephanie Fu and Lukas Muttenthaler and Netanel Yakir Tamir and Lucy Chai and Simon Kornblith and Trevor Darrell and Phillip Isola},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NmlnmLYMZ4}
} | Humans judge perceptual similarity according to diverse visual attributes, including scene layout, subject location, and camera pose. Existing vision models understand a wide range of semantic abstractions but improperly weigh these attributes and thus make inferences misaligned with human perception.
While vision representations have previously benefited from human preference alignment in contexts like image generation, the utility of perceptually aligned representations in more general-purpose settings remains unclear. Here, we investigate how aligning vision model representations to human perceptual judgments impacts their usability in standard computer vision tasks. We finetune state-of-the-art models on a dataset of human similarity judgments for synthetic image triplets and evaluate them across diverse computer vision tasks. We find that aligning models to perceptual judgments yields representations that improve upon the original backbones across many downstream tasks, including counting, semantic segmentation, depth estimation, instance retrieval, and retrieval-augmented generation. In addition, we find that performance is widely preserved on other tasks, including specialized out-of-distribution domains such as in medical imaging and 3D environment frames. Our results suggest that injecting an inductive bias about human perceptual knowledge into vision models can make them better representation learners. | When does perceptual alignment benefit vision representations? | [
"Shobhita Sundaram",
"Stephanie Fu",
"Lukas Muttenthaler",
"Netanel Yakir Tamir",
"Lucy Chai",
"Simon Kornblith",
"Trevor Darrell",
"Phillip Isola"
] | NeurIPS.cc/2024/Conference | 2410.10817 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=NlpHKNjNNZ | @inproceedings{
chang2024just,
title={Just Add \$100 More: Augmenting Pseudo-Li{DAR} Point Cloud for Resolving Class-imbalance Problem},
author={Mincheol Chang and Siyeong Lee and Jinkyu Kim and Namil Kim},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NlpHKNjNNZ}
} | Typical LiDAR-based 3D object detection models are trained with real-world data collection, which is often imbalanced over classes.
To deal with it, augmentation techniques are commonly used, such as copying ground truth LiDAR points and pasting them into scenes.
However, existing methods struggle with the lack of sample diversity for minority classes and the limitation of suitable placement.
In this work, we introduce a novel approach that utilizes pseudo LiDAR point clouds generated from low-cost miniatures or real-world videos, which is called Pseudo Ground Truth augmentation (PGT-Aug).
PGT-Aug involves three key steps: (i) volumetric 3D instance reconstruction using a 2D-to-3D view synthesis model, (ii) object-level domain alignment with LiDAR intensity simulation, and (iii) a hybrid context-aware placement method from ground and map information.
We demonstrate the superiority and generality of our method through performance improvements in extensive experiments conducted on popular benchmarks, i.e., nuScenes, KITTI, and Lyft, especially for the datasets with large domain gaps captured by different LiDAR configurations.
The project webpage is https://just-add-100-more.github.io. | Just Add 100 More: Augmenting Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem | [
"Mincheol Chang",
"Siyeong Lee",
"Jinkyu Kim",
"Namil Kim"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NkuySm8qVs | @inproceedings{
lehre2024no,
title={No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation},
author={Per Kristian Lehre and Shishen Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NkuySm8qVs}
} | Black-box optimisation is one of the important areas in optimisation. The original No Free Lunch (NFL) theorems highlight the limitations of traditional black-box optimisation and learning algorithms, serving as a theoretical foundation for traditional optimisation. No Free Lunch Analysis in adversarial (also called maximin) optimisation is a long-standing problem [45 , 46]. This paper first rigorously proves a (NFL) Theorem for general black-box adversarial optimisation when considering Pure Strategy Nash Equilibrium (NE) as the solution concept. We emphasise the solution concept (i.e. define the optimality in adversarial optimisation) as the key in our NFL theorem. In particular, if Nash Equilibrium is considered as the solution concept and the cost of the algorithm is measured in terms of the number of columns and rows queried in the payoff matrix, then the average performance of all black-box adversarial optimisation algorithms is the same. Moreover, we first introduce black-box complexity to analyse the black-box adversarial optimisation algorithm. We employ Yao’s Principle and our new NFL Theorem to provide general lower bounds for the query complexity of finding a Nash Equilibrium in adversarial optimisation. Finally, we illustrate the practical ramifications of our results on simple two-player zero-sum games. More specifically, no black-box optimisation algorithm for finding the unique Nash equilibrium in two-player zero-sum games can exceed logarithmic complexity relative to search space size. Meanwhile, no black-box algorithm can solve any bimatrix game with unique NE with fewer than a linear number of queries in the size of the payoff matrix. | No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation | [
"Per Kristian Lehre",
"Shishen Lin"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NkXuAOygXN | @inproceedings{
cao2024testtime,
title={Test-Time Dynamic Image Fusion},
author={Bing Cao and Yinan Xia and Yi Ding and Changqing Zhang and Qinghua Hu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NkXuAOygXN}
} | The inherent challenge of image fusion lies in capturing the correlation of multi-source images and comprehensively integrating effective information from different sources. Most existing techniques fail to perform dynamic image fusion while notably lacking theoretical guarantees, leading to potential deployment risks in this field. Is it possible to conduct dynamic image fusion with a clear theoretical justification? In this paper, we give our solution from a generalization perspective. We proceed to reveal the generalized form of image fusion and derive a new test-time dynamic image fusion paradigm. It provably reduces the upper bound of generalization error. Specifically, we decompose the fused image into multiple components corresponding to its source data. The decomposed components represent the effective information from the source data, thus the gap between them reflects the \textit{Relative Dominability} (RD) of the uni-source data in constructing the fusion image. Theoretically, we prove that the key to reducing generalization error hinges on the negative correlation between the RD-based fusion weight and the uni-source reconstruction loss. Intuitively, RD dynamically highlights the dominant regions of each source and can be naturally converted to the corresponding fusion weight, achieving robust results. Extensive experiments and discussions with in-depth analysis on multiple benchmarks confirm our findings and superiority. Our code is available at https://github.com/Yinan-Xia/TTD. | Test-Time Dynamic Image Fusion | [
"Bing Cao",
"Yinan Xia",
"Yi Ding",
"Changqing Zhang",
"Qinghua Hu"
] | NeurIPS.cc/2024/Conference | 2411.02840 | [
"https://github.com/yinan-xia/ttd"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=NjewXJUDYq | @inproceedings{
kim2024paralinguisticsaware,
title={Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation},
author={Heeseung Kim and Soonshin Seo and Kyeongseok Jeong and Ohsung Kwon and Soyoon Kim and Jungwhan Kim and Jaehong Lee and Eunwoo Song and Myungwoo Oh and Jung-Woo Ha and Sungroh Yoon and Kang Min Yoo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NjewXJUDYq}
} | Recent work shows promising results in expanding the capabilities of large language models (LLM) to directly understand and synthesize speech. However, an LLM-based strategy for modeling spoken dialogs remains elusive, calling for further investigation. This paper introduces an extensive speech-text LLM framework, the Unified Spoken Dialog Model (USDM), designed to generate coherent spoken responses with naturally occurring prosodic features relevant to the given input speech without relying on explicit automatic speech recognition (ASR) or text-to-speech (TTS) systems. We have verified the inclusion of prosody in speech tokens that predominantly contain semantic information and have used this foundation to construct a prosody-infused speech-text model. Additionally, we propose a generalized speech-text pretraining scheme that enhances the capture of cross-modal semantics. To construct USDM, we fine-tune our speech-text model on spoken dialog data using a multi-step spoken dialog template that stimulates the chain-of-reasoning capabilities exhibited by the underlying LLM. Automatic and human evaluations on the DailyTalk dataset demonstrate that our approach effectively generates natural-sounding spoken responses, surpassing previous and cascaded baselines. Our code and checkpoints are available at https://github.com/naver-ai/usdm. | Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation | [
"Heeseung Kim",
"Soonshin Seo",
"Kyeongseok Jeong",
"Ohsung Kwon",
"Soyoon Kim",
"Jungwhan Kim",
"Jaehong Lee",
"Eunwoo Song",
"Myungwoo Oh",
"Jung-Woo Ha",
"Sungroh Yoon",
"Kang Min Yoo"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NidGPsP0Qq | @inproceedings{
zhang2024provably,
title={Provably Efficient Interactive-Grounded Learning with Personalized Reward},
author={Mengxiao Zhang and Yuheng Zhang and Haipeng Luo and Paul Mineiro},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NidGPsP0Qq}
} | Interactive-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in which a learner aims at maximizing unobservable rewards through interacting with an environment and observing reward-dependent feedback on the taken actions.
To deal with personalized rewards that are ubiquitous in applications such as recommendation systems, Maghakian et al. [2022] study a version of IGL with context-dependent feedback, but their algorithm does not come with theoretical guarantees. In this work, we consider the same problem and provide the first provably efficient algorithms with sublinear regret under realizability. Our analysis reveals that the step-function estimator of prior work can deviate uncontrollably due to finite-sample effects. Our solution is a novel Lipschitz reward estimator which underestimates the true reward and enjoys favorable generalization performances. Building on this estimator, we propose two algorithms, one based on explore-then-exploit and the other based on inverse-gap weighting. We apply IGL to learning from image feedback and learning from text feedback, which are reward-free settings that arise in practice. Experimental results showcase the importance of using our Lipschitz reward estimator and the overall effectiveness of our algorithms. | Provably Efficient Interactive-Grounded Learning with Personalized Reward | [
"Mengxiao Zhang",
"Yuheng Zhang",
"Haipeng Luo",
"Paul Mineiro"
] | NeurIPS.cc/2024/Conference | 2405.20677 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Ni9kebsSTt | @inproceedings{
li2024nearest,
title={Nearest Neighbor Speculative Decoding for {LLM} Generation and Attribution},
author={Minghan Li and Xilun Chen and Ari Holtzman and Beidi Chen and Jimmy Lin and Wen-tau Yih and Xi Victoria Lin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Ni9kebsSTt}
} | Large language models (LLMs) often hallucinate and lack the ability to provide attribution for their generations. Semi-parametric LMs, such as kNN-LM, approach these limitations by refining the output of an LM for a given prompt using its nearest neighbor matches in a non-parametric data store. However, these models often exhibit slow inference speeds and produce non-fluent texts. In this paper, we introduce Nearest Neighbor Speculative Decoding (NEST), a novel semi-parametric language modeling approach that is capable of incorporating real-world text spans of arbitrary length into the LM generations and providing attribution to their sources. NEST performs token-level retrieval at each inference step to compute a semi-parametric mixture distribution and identify promising span continuations in a corpus. It then uses an approximate speculative decoding procedure that accepts a prefix of the retrieved span or generates a new token. NEST significantly enhances the generation quality and attribution rate of the base LM across a variety of knowledge-intensive tasks, surpassing the conventional kNN-LM method and performing competitively with in-context retrieval augmentation. In addition, NEST substantially improves the generation speed, achieving a 1.8x speedup in inference time when applied to Llama-2-Chat 70B. Code will be released at https://github.com/facebookresearch/NEST/tree/main. | Nearest Neighbor Speculative Decoding for LLM Generation and Attribution | [
"Minghan Li",
"Xilun Chen",
"Ari Holtzman",
"Beidi Chen",
"Jimmy Lin",
"Wen-tau Yih",
"Xi Victoria Lin"
] | NeurIPS.cc/2024/Conference | 2405.19325 | [
""
] | https://huggingface.co/papers/2405.19325 | 5 | 13 | 0 | 7 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=NhyDfZXjQX | @inproceedings{
li2024a,
title={A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs},
author={Haoxuan Li and Yue Liu and Zhi Geng and Kun Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NhyDfZXjQX}
} | Developing fair automated machine learning algorithms is critical in making safe and trustworthy decisions. Many causality-based fairness notions have been proposed to address the above issues by quantifying the causal connections between sensitive attributes and decisions, and when the true causal graph is fully known, certain algorithms that achieve interventional fairness have been proposed. However, when the true causal graph is unknown, it is still challenging to effectively and efficiently exploit partially directed acyclic graphs (PDAGs) to achieve interventional fairness. To exploit the PDAGs for achieving interventional fairness, previous methods have been built on variable selection or causal effect identification, but limited to reduced prediction accuracy or strong assumptions. In this paper, we propose a general min-max optimization framework that can achieve interventional fairness with promising prediction accuracy and can be extended to maximally oriented PDAGs (MPDAGs) with added background knowledge. Specifically, we first estimate all possible treatment effects of sensitive attributes on a given prediction model from all possible adjustment sets of sensitive attributes via an efficient local approach. Next, we propose to alternatively update the prediction model and possible estimated causal effects, where the prediction model is trained via a min-max loss to control the worst-case fairness violations. Extensive experiments on synthetic and real-world datasets verify the superiority of our methods. To benefit the research community, we have released our project at https://github.com/haoxuanli-pku/NeurIPS24-Interventional-Fairness-with-PDAGs. | A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs | [
"Haoxuan Li",
"Yue Liu",
"Zhi Geng",
"Kun Zhang"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NhucGZtikE | @inproceedings{
jeffares2024deep,
title={Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting \& Beyond},
author={Alan Jeffares and Alicia Curth and Mihaela van der Schaar},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NhucGZtikE}
} | Deep learning sometimes appears to work in unexpected ways. In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network consisting of a sequence of first-order approximations telescoping out into a single empirically operational tool for practical analysis. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena in the literature -- including double descent, grokking, linear mode connectivity, and the challenges of applying deep learning on tabular data -- highlighting that this model allows us to construct and extract metrics that help predict and understand the a priori unexpected performance of neural networks. We also demonstrate that this model presents a pedagogical formalism allowing us to isolate components of the training process even in complex contemporary settings, providing a lens to reason about the effects of design choices such as architecture & optimization strategy, and reveals surprising parallels between neural network learning and gradient boosting. | Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting Beyond | [
"Alan Jeffares",
"Alicia Curth",
"Mihaela van der Schaar"
] | NeurIPS.cc/2024/Conference | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NhtBXSNXKA | @inproceedings{
hu2024singleloop,
title={Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions},
author={Quanqi Hu and Qi Qi and Zhaosong Lu and Tianbao Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NhtBXSNXKA}
} | In this paper, we study a class of non-smooth non-convex problems in the form of $\min_{x}[\max_{y\in\mathcal Y}\phi(x, y) - \max_{z\in\mathcal Z}\psi(x, z)]$, where both $\Phi(x) = \max_{y\in\mathcal Y}\phi(x, y)$ and $\Psi(x)=\max_{z\in\mathcal Z}\psi(x, z)$ are weakly convex functions, and $\phi(x, y), \psi(x, z)$ are strongly concave functions in terms of $y$ and $z$, respectively. It covers two families of problems that have been studied but are missing single-loop stochastic algorithms, i.e., difference of weakly convex functions and weakly convex strongly-concave min-max problems. We propose a stochastic Moreau envelope approximate gradient method dubbed SMAG, the first single-loop algorithm for solving these problems, and provide a state-of-the-art non-asymptotic convergence rate. The key idea of the design is to compute an approximate gradient of the Moreau envelopes of $\Phi, \Psi$ using only one step of stochastic gradient update of the primal and dual variables. Empirically, we conduct experiments on positive-unlabeled (PU) learning and partial area under ROC curve (pAUC) optimization with an adversarial fairness regularizer to validate the effectiveness of our proposed algorithms. | Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions | [
"Quanqi Hu",
"Qi Qi",
"Zhaosong Lu",
"Tianbao Yang"
] | NeurIPS.cc/2024/Conference | 2405.18577 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=NhqZpst42I | @inproceedings{
fel2024understanding,
title={Understanding Visual Feature Reliance through the Lens of Complexity},
author={Thomas FEL and Louis B{\'e}thune and Andrew Kyle Lampinen and Thomas Serre and Katherine Hermann},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NhqZpst42I}
} | Recent studies suggest that deep learning models' inductive bias towards favoring simpler features may be an origin of shortcut learning. Yet, there has been limited focus on understanding the complexities of the myriad features that models learn. In this work, we introduce a new metric for quantifying feature complexity, based on V-information and capturing whether a feature requires complex computational transformations to be extracted. Using this V-information metric, we analyze the complexities of 10,000 features—represented as directions in the penultimate layer—that were extracted from a standard ImageNet-trained vision model. Our study addresses four key questions:
First, we ask what features look like as a function of complexity, and find a spectrum of simple-to-complex features present within the model. Second, we ask when features are learned during training. We find that simpler features dominate early in training, and more complex features emerge gradually. Third, we investigate where within the network simple and complex features "flow," and find that simpler features tend to bypass the visual hierarchy via residual connections. Fourth, we explore the connection between features' complexity and their importance for driving the network's decision. We find that complex features tend to be less important. Surprisingly, important features become accessible at earlier layers during training, like a "sedimentation process," allowing the model to build upon these foundational elements. | Understanding Visual Feature Reliance through the Lens of Complexity | [
"Thomas FEL",
"Louis Béthune",
"Andrew Kyle Lampinen",
"Thomas Serre",
"Katherine Hermann"
] | NeurIPS.cc/2024/Conference | 2407.06076 | [
""
] | https://huggingface.co/papers/2407.06076 | 3 | 5 | 1 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=NgyT80IPUK | @inproceedings{
zhang2024matrix,
title={Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods},
author={Yihan Zhang and Marco Mondelli},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NgyT80IPUK}
} | We study the matrix denoising problem of estimating the singular vectors of a rank-$1$ signal corrupted by noise with both column and row correlations. Existing works are either unable to pinpoint the exact asymptotic estimation error or, when they do so, the resulting approaches (e.g., based on whitening or singular value shrinkage) remain vastly suboptimal. On top of this, most of the literature has focused on the special case of estimating the left singular vector of the signal when the noise only possesses row correlation (one-sided heteroscedasticity). In contrast, our work establishes the information-theoretic and algorithmic limits of matrix denoising with doubly heteroscedastic noise. We characterize the exact asymptotic minimum mean square error, and design a novel spectral estimator with rigorous optimality guarantees: under a technical condition, it attains positive correlation with the signals whenever information-theoretically possible and, for one-sided heteroscedasticity, it also achieves the Bayes-optimal error. Numerical experiments demonstrate the significant advantage of our theoretically principled method with the state of the art. The proofs draw connections with statistical physics and approximate message passing, departing drastically from standard random matrix theory techniques. | Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods | [
"Yihan Zhang",
"Marco Mondelli"
] | NeurIPS.cc/2024/Conference | 2405.13912 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=NfOFbPpYII | @inproceedings{
huang2024nonasymptotic,
title={Non-asymptotic Convergence of Training Transformers for Next-token Prediction},
author={Ruiquan Huang and Yingbin Liang and Jing Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NfOFbPpYII}
} | Transformers have achieved extraordinary success in modern machine learning due to their excellent ability to handle sequential data, especially in next-token prediction (NTP) tasks. However, the theoretical understanding of their performance in NTP is limited, with existing studies focusing mainly on asymptotic performance. This paper provides a fine-grained non-asymptotic analysis of the training dynamics of a one-layer transformer consisting of a self-attention module followed by a feed-forward layer. We first characterize the essential structural properties of training datasets for NTP using a mathematical framework based on partial orders.
Then, we design a two-stage training algorithm, where the pre-processing stage for training the feed-forward layer and the main stage for training the attention layer exhibit fast convergence performance. Specifically, both layers converge sub-linearly to the direction of their corresponding max-margin solutions. We also show that the cross-entropy loss enjoys a linear convergence rate. Furthermore, we show that the trained transformer presents non-trivial prediction ability with dataset shift, which sheds light on the remarkable generalization performance of transformers. Our analysis technique involves the development of novel properties on the attention gradient and further in-depth analysis of how these properties contribute to the convergence of the training process. Our experiments further validate our theoretical findings. | Non-asymptotic Convergence of Training Transformers for Next-token Prediction | [
"Ruiquan Huang",
"Yingbin Liang",
"Jing Yang"
] | NeurIPS.cc/2024/Conference | 2409.17335 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Nf4MHF1pi5 | @inproceedings{
yang2024watch,
title={Watch Out for Your Agents! Investigating Backdoor Threats to {LLM}-Based Agents},
author={Wenkai Yang and Xiaohan Bi and Yankai Lin and Sishuo Chen and Jie Zhou and Xu Sun},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Nf4MHF1pi5}
} | Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and shopping, etc. It is crucial to ensure the reliability and security of LLM-based agents during applications. However, the safety issues of LLM-based agents are currently under-explored. In this work, we take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. We first formulate a general framework of agent backdoor attacks, then we present a thorough analysis of different forms of agent backdoor attacks. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms: (1) From the perspective of the final attacking outcomes, the agent backdoor attacker can not only choose to manipulate the final output distribution, but also introduce the malicious behavior in an intermediate reasoning step only, while keeping the final output correct. (2) Furthermore, the former category can be divided into two subcategories based on trigger locations, in which the backdoor trigger can either be hidden in the user query or appear in an intermediate observation returned by the external environment. We implement the above variations of agent backdoor attacks on two typical agent tasks including web shopping and tool utilization. Extensive experiments show that LLM-based agents suffer severely from backdoor attacks and such backdoor vulnerability cannot be easily mitigated by current textual backdoor defense algorithms. This indicates an urgent need for further research on the development of targeted defenses against backdoor attacks on LLM-based agents. Warning: This paper may contain biased content. | Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents | [
"Wenkai Yang",
"Xiaohan Bi",
"Yankai Lin",
"Sishuo Chen",
"Jie Zhou",
"Xu Sun"
] | NeurIPS.cc/2024/Conference | 2402.11208 | [
"https://github.com/lancopku/agent-backdoor-attacks"
] | https://huggingface.co/papers/2402.11208 | 1 | 0 | 0 | 6 | [] | [] | [] | [] | [] | [] | 1 | poster |
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