NeurIPS 2023

3,540 papers

Synthcity: a benchmark framework for diverse use cases of tabular synthetic data

Zhaozhi Qian, Rob Davis, Mihaela van der Schaar

Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples

Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar

GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search

Xiao Zang, Miao Yin, Jinqi Xiao, Saman Zonouz, Bo Yuan

MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining

Jacob Portes, Alexander Trott, Sam Havens, DANIEL KING, Abhinav Venigalla

Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples

Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Mehran Kazemi

Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks

Andong Wang, CHAO LI, Mingyuan Bai, Zhong Jin, Guoxu Zhou

Bayesian Optimization with Cost-varying Variable Subsets

Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low

Evolving Connectivity for Recurrent Spiking Neural Networks

Guan Wang, Yuhao Sun, Sijie Cheng, Sen Song

Information Maximization Perspective of Orthogonal Matching Pursuit with Applications to Explainable AI

Aditya Chattopadhyay, Ryan Pilgrim, René Vidal

Neural Lyapunov Control for Discrete-Time Systems

Junlin Wu, Andrew Clark, Yiannis Kantaros, Yevgeniy Vorobeychik

InstanT: Semi-supervised Learning with Instance-dependent Thresholds

Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability

Haotian Xue, Alexandre Araujo, Bin Hu, Yongxin Chen

Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewards

Silviu Pitis

Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation

Yatong Sun, Bin Wang, Zhu Sun, Xiaochun Yang, Yan Wang

Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields

Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park

HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception

Peiyan Dong, Zhenglun Kong, Xin Meng, Pinrui Yu, Yifan Gong

Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi

Estimating the Rate-Distortion Function by Wasserstein Gradient Descent

Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt

Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking

Frederik Kunstner, Victor Sanches Portella, Mark Schmidt, Nicholas Harvey

Parts of Speech–Grounded Subspaces in Vision-Language Models

James Oldfield, Christos Tzelepis, Yannis Panagakis, Mihalis Nicolaou, Ioannis Patras

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

Brian Zhang, Gabriele Farina, Ioannis Anagnostides, Federico Cacciamani, Stephen Mcaleer

An Efficient End-to-End Training Approach for Zero-Shot Human-AI Coordination

Xue Yan, Jiaxian Guo, Xingzhou Lou, Jun Wang, Haifeng Zhang

Mutual-Information Regularized Multi-Agent Policy Iteration

Wang, Deheng Ye, Zongqing Lu

Double Gumbel Q-Learning

David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon

Prototypical Variational Autoencoder for 3D Few-shot Object Detection

Weiliang Tang, Biqi YANG, Xianzhi Li, Yun-Hui Liu, Pheng-Ann Heng

Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen

$SE(3)$ Equivariant Convolution and Transformer in Ray Space

Yinshuang Xu, Jiahui Lei, Kostas Daniilidis

Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach

Fabian Zaiser, Andrzej Murawski, Chih-Hao Luke Ong

ForecastPFN: Synthetically-Trained Zero-Shot Forecasting

Samuel Dooley, Gurnoor Singh Khurana, Chirag Mohapatra, Siddartha V Naidu, Colin White

A Bounded Ability Estimation for Computerized Adaptive Testing

Yan Zhuang, Qi Liu, Guanhao Zhao, Zhenya Huang, Weizhe Huang

Uncovering and Quantifying Social Biases in Code Generation

Yan Liu, Xiaokang Chen, Yan Gao, Zhe Su, Fengji Zhang

Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context

Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan

Honesty Is the Best Policy: Defining and Mitigating AI Deception

Francis Ward, Francesca Toni, Francesco Belardinelli, Tom Everitt

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning

Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun

Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

Mostafa Dehghani, Basil Mustafa, Josip Djolonga, Jonathan Heek, Matthias Minderer

Optimistic Rates for Multi-Task Representation Learning

Austin Watkins, Enayat Ullah, Thanh Nguyen-Tang, Raman Arora

UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models

Xin Li, Sima Behpour, Thang Long Doan, Wenbin He, Liang Gou

The Distortion of Binomial Voting Defies Expectation

Yannai A. Gonczarowski, Gregory Kehne, Ariel D Procaccia, Ben Schiffer, Shirley Zhang

Regularized Behavior Cloning for Blocking the Leakage of Past Action Information

Seokin Seo, HyeongJoo Hwang, Hongseok Yang, Kee-eung Kim

On the Generalization Properties of Diffusion Models

Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian

Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection

Taehyeon Kim, Eric Lin, Junu Lee, Christian Lau, Vaikkunth Mugunthan

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu

Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks

Honghao Wei, Xin Liu, Weina Wang, Lei Ying

Identification of Nonlinear Latent Hierarchical Models

Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi

Collaborative Learning via Prediction Consensus

Dongyang Fan, Celestine Mendler-Dünner, Martin Jaggi

Towards Unbounded Machine Unlearning

Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, Eleni Triantafillou

Compression with Bayesian Implicit Neural Representations

Zongyu Guo, Gergely Flamich, Jiajun He, Zhibo Chen, José Miguel Hernández-Lobato

Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition

Yiting Dong, Yang Li, Dongcheng Zhao, Guobin Shen, Yi Zeng

Rethinking the Backward Propagation for Adversarial Transferability

Wang Xiaosen, Kangheng Tong, Kun He

Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data

Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models

Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa

Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis

Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang

Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation

Haonan Wang, Xiaomeng Li

Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent

Lingjiong Zhu, Mert Gurbuzbalaban, Anant Raj, Umut Simsekli

Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces

Leonard Papenmeier, Luigi Nardi, Matthias Poloczek

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang

Optimal approximation using complex-valued neural networks

Paul Geuchen, Felix Voigtlaender

Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning

James Queeney, Mouhacine Benosman

A generative model of the hippocampal formation trained with theta driven local learning rules

Tom M George, Kimberly L Stachenfeld, Caswell Barry, Claudia Clopath, Tomoki Fukai

M^4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models

Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju

On the Robustness of Mechanism Design under Total Variation Distance

Anuran Makur, Marios Mertzanidis, Alexandros Psomas, Athina Terzoglou

What You See is What You Read? Improving Text-Image Alignment Evaluation

Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig

A Variational Perspective on High-Resolution ODEs

Hoomaan Maskan, Konstantinos Zygalakis, Alp Yurtsever

Birth of a Transformer: A Memory Viewpoint

Alberto Bietti, Vivien Cabannes, Diane Bouchacourt, Herve Jegou, Léon Bottou

Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs

Rajat Vadiraj Dwaraknath, Tolga Ergen, Mert Pilanci

From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion

Robin San Roman, Yossi Adi, Antoine Deleforge, Romain Serizel, Gabriel Synnaeve

Minimum Description Length and Generalization Guarantees for Representation Learning

Milad Sefidgaran, Abdellatif Zaidi, Piotr Krasnowski

FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow

Cameron Smith, Yilun Du, Ayush Tewari, Vincent Sitzmann

Uncovering Neural Scaling Laws in Molecular Representation Learning

Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li

Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent

Kruno Lehman, Alain Durmus, Umut Simsekli

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria

Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran

Robust Learning with Progressive Data Expansion Against Spurious Correlation

Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu

Emergent Correspondence from Image Diffusion

Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, Bharath Hariharan

Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks

Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John Dickerson, Joseph Suarez

Partial Multi-Label Learning with Probabilistic Graphical Disambiguation

Jun-Yi Hang, Min-ling Zhang

Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems

Junyi Li, Feihu Huang, Heng Huang

Towards Free Data Selection with General-Purpose Models

Yichen Xie, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan

Zero-Regret Performative Prediction Under Inequality Constraints

Wenjing YAN, Xuanyu Cao

Passive learning of active causal strategies in agents and language models

Andrew Lampinen, Stephanie Chan, Ishita Dasgupta, Andrew Nam, Jane Wang

Training Your Image Restoration Network Better with Random Weight Network as Optimization Function

man zhou, Naishan Zheng, Yuan Xu, Chun-Le Guo, Chongyi Li

Adversarial Model for Offline Reinforcement Learning

Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng

Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis

Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Taesu Kim, Daehyun Ahn

On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective

Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama

Towards Higher Ranks via Adversarial Weight Pruning

Yuchuan Tian, Hanting Chen, Tianyu Guo, Chao Xu, Yunhe Wang

Tools for Verifying Neural Models' Training Data

Dami Choi, Yonadav Shavit, David K. Duvenaud

Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features

Mingli Zhu, Shaokui Wei, Hongyuan Zha, Baoyuan Wu

Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration

Jie Xu, Shuo Chen, Yazhou Ren, Xiaoshuang Shi, Hengtao Shen

Agnostic Multi-Group Active Learning

Nicholas Rittler, Kamalika Chaudhuri

Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability

Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy

Taylor TD-learning

Michele Garibbo, Maxime Robeyns, Laurence Aitchison

Feature-Learning Networks Are Consistent Across Widths At Realistic Scales

Nikhil Vyas, Alexander Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan

Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization

Kaiyue Wen, Zhiyuan Li, Tengyu Ma

Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals

Yue Wu, Yewen Fan, Paul Pu Liang, Amos Azaria, Yuanzhi Li

Projection-Free Online Convex Optimization via Efficient Newton Iterations

Khashayar Gatmiry, Zak Mhammedi

Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel

Valerii Likhosherstov, Krzysztof M Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamas Sarlos

Learning better with Dale’s Law: A Spectral Perspective

Pingsheng Li, Jonathan Cornford, Arna Ghosh, Blake Richards

Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking

Mohammad Mahdi Kamani, Yuhang Yao, Hanjia Lyu, Zhongwei Cheng, Lin Chen

Tight Bounds for Volumetric Spanners and Applications

Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian

Minimax-Optimal Location Estimation

Shivam Gupta, Jasper Lee, ecprice, Paul Valiant

Conformal Prediction Sets for Ordinal Classification

Prasenjit Dey, Srujana Merugu, Sivaramakrishnan R Kaveri