NeurIPS 2023

3,540 papers

Meta-Learning Adversarial Bandit Algorithms

Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina F Balcan, Kfir Y. Levy

Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams

Esmaeil Seraj, Jerry Xiong, Mariah Schrum, Matthew Gombolay

FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations

Chanakya Ekbote, Ajinkya Deshpande, Arun Iyer, SUNDARARAJAN SELLAMANICKAM, Ramakrishna Bairi

Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices

Kilian Pfeiffer, Ramin Khalili, Joerg Henkel

Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics

Guillaume Mahey, Laetitia Chapel, Gilles Gasso, Clément Bonet, Nicolas Courty

Concept Algebra for (Score-Based) Text-Controlled Generative Models

Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch

Quantum speedups for stochastic optimization

Aaron Sidford, Chenyi Zhang

Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks

Jun Yin, Chaozhuo Li, Hao Yan, Jianxun Lian, Senzhang Wang

On the Sublinear Regret of GP-UCB

Justin Whitehouse, Aaditya Ramdas, Steven Z. Wu

Regret Minimization via Saddle Point Optimization

Johannes Kirschner, Alireza Bakhtiari, Kushagra Chandak, Volodymyr Tkachuk, Csaba Szepesvári

Simplifying Neural Network Training Under Class Imbalance

Ravid Shwartz-Ziv, Micah Goldblum, Yucen Li, C. Bayan Bruss, Andrew G Wilson

PHOTOSWAP: Personalized Subject Swapping in Images

Jing Gu, Yilin Wang, Nanxuan Zhao, Tsu-Jui Fu, Wei Xiong

Adaptive Algorithms for Relaxed Pareto Set Identification

Cyrille KONE, Emilie Kaufmann, Laura Richert

Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?

Erin George, Michael Murray, William Swartworth, Deanna Needell

Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms

Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason Lee

A Comprehensive Benchmark for Neural Human Radiance Fields

Kenkun Liu, Derong Jin, Ailing Zeng, Xiaoguang Han, Lei Zhang

Demystifying Oversmoothing in Attention-Based Graph Neural Networks

Xinyi Wu, Amir Ajorlou, Zihui Wu, Ali Jadbabaie

Conservative State Value Estimation for Offline Reinforcement Learning

Liting Chen, Jie Yan, Zhengdao Shao, Lu Wang, Qingwei Lin

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks

Feng Chen, Daniel Kunin, Atsushi Yamamura, Surya Ganguli

Counting Distinct Elements Under Person-Level Differential Privacy

Thomas Steinke, Alexander Knop

Expressive probabilistic sampling in recurrent neural networks

Shirui Chen, Linxing Jiang, Rajesh PN Rao, Eric Shea-Brown

What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?

Fnu Suya, Xiao Zhang, Yuan Tian, David Evans

Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry

Bariscan Bozkurt, Cengiz Pehlevan, Alper Erdogan

A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging

Rui Wang, Yanyan Ouyang, Yu Panpan, Wangli Xu

Visual Instruction Tuning

Haotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae Lee

HIQL: Offline Goal-Conditioned RL with Latent States as Actions

Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine

XAGen: 3D Expressive Human Avatars Generation

Zhongcong XU, Jianfeng Zhang, Jun Hao Liew, Jiashi Feng, Mike Zheng Shou

Learning Exponential Families from Truncated Samples

Jane Lee, Andre Wibisono, Emmanouil Zampetakis

RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization

Siqi Shen, Chennan Ma, CHAO LI, Weiquan Liu, Yongquan Fu

On Private and Robust Bandits

Yulian Wu, Xingyu Zhou, Youming Tao, Di Wang

OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment

Yiheng Zhu, Yang Zhan, Xuankun Huang, Yuwei Chen, yujie Chen

Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models

Leonardo Galli, Holger Rauhut, Mark Schmidt

NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos

Jinxi Li, Ziyang Song, Bo Yang

Uncovering motifs of concurrent signaling across multiple neuronal populations

Evren Gokcen, Anna Jasper, Alison Xu, Adam Kohn, Christian K. Machens

H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng

Approximately Equivariant Graph Networks

Ningyuan Huang, Ron Levie, Soledad Villar

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration

Haitao Lin, Yufei Huang, Odin Zhang, Yunfan Liu, Lirong Wu

Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification

Tianjun Ke, Haoqun Cao, Zenan Ling, Feng Zhou

A Causal Framework for Decomposing Spurious Variations

Drago Plecko, Elias Bareinboim

Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network

Yixiao Zhou, Ruiqi Jia, Hongxiang Lin, Hefeng Quan, Yumeng Zhao

Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond

Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda

Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates

Sarath Sreedharan, Michael Katz

Subspace Identification for Multi-Source Domain Adaptation

Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao

Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection

Haibao Yu, Yingjuan Tang, Enze Xie, Jilei Mao, Ping Luo

Unpaired Multi-Domain Causal Representation Learning

Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler

Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization

Mahyar Fazlyab, Taha Entesari, Aniket Roy, Rama Chellappa

Generalised f-Mean Aggregation for Graph Neural Networks

Ryan Kortvelesy, Steven Morad, Amanda Prorok

CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation

Xiuzhe Wu, Peng Dai, Weipeng DENG, Handi Chen, Yang Wu

Fast and Simple Spectral Clustering in Theory and Practice

Peter Macgregor

Robust Mean Estimation Without Moments for Symmetric Distributions

Gleb Novikov, David Steurer, Stefan Tiegel

From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces

Peter Shaw, Mandar Joshi, James Cohan, Jonathan Berant, Panupong Pasupat

NPCL: Neural Processes for Uncertainty-Aware Continual Learning

Saurav Jha, Dong Gong, He Zhao, Lina Yao

Cookie Consent Has Disparate Impact on Estimation Accuracy

Erik Miehling, Rahul Nair, Elizabeth Daly, Karthikeyan Natesan Ramamurthy, Robert Redmond

Self-supervised Graph Neural Networks via Low-Rank Decomposition

Liang Yang, Runjie Shi, Qiuliang Zhang, bingxin niu, Zhen Wang

Intriguing Properties of Quantization at Scale

Arash Ahmadian, Saurabh Dash, Hongyu Chen, Bharat Venkitesh, Zhen Stephen Gou

Learning To Dive In Branch And Bound

Max Paulus, Andreas Krause

Glance and Focus: Memory Prompting for Multi-Event Video Question Answering

Ziyi Bai, Ruiping Wang, Xilin Chen

Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements

Gaurav Shrivastava, Ser Nam Lim, Abhinav Shrivastava

Data Selection for Language Models via Importance Resampling

Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang

Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage

Yuheng Jia, Fuchao Yang, Yongqiang Dong

Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents

Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian (Shawn) Ma

Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance

Congyue Deng, Jiahui Lei, William B Shen, Kostas Daniilidis, Leonidas Guibas

Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness

Jacy Anthis, Victor Veitch

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds

Paul Rosa, Slava Borovitskiy, Alexander Terenin, Judith Rousseau

Characteristic Circuits

Zhongjie Yu, Martin Trapp, Kristian Kersting

Improving Self-supervised Molecular Representation Learning using Persistent Homology

Yuankai Luo, Lei Shi, Veronika Thost

UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures

Zhong-Qiu Wang, Shinji Watanabe

Learning and processing the ordinal information of temporal sequences in recurrent neural circuits

Xiaolong Zou, Zhikun Chu, Qinghai Guo, Jie Cheng, Bo Ho

An Information Theory Perspective on Variance-Invariance-Covariance Regularization

Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun

VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models

Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho

Triple Eagle: Simple, Fast and Practical Budget-Feasible Mechanisms

Kai Han, You Wu, He Huang, Shuang Cui

A Randomized Approach to Tight Privacy Accounting

Jiachen (Tianhao) Wang, Saeed Mahloujifar, Tong Wu, Ruoxi Jia, Prateek Mittal

H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets

Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis

Mitchell Ostrow, Adam Eisen, Leo Kozachkov, Ila Fiete

Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark

Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic

VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning

Jiayi Guan, Guang Chen, Jiaming Ji, Long Yang, ao zhou

Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels

Zhen Liu, ma peitian, Dongliang Chen, Wenbin Pei, Qianli Ma

Probabilistic Inference in Reinforcement Learning Done Right

Jean Tarbouriech, Tor Lattimore, Brendan O'Donoghue

Tanimoto Random Features for Scalable Molecular Machine Learning

Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures

Runa Eschenhagen, Alexander Immer, Richard Turner, Frank Schneider, Philipp Hennig

GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

Xin Zheng, Miao Zhang, Chunyang Chen, Soheila Molaei, Chuan Zhou

Mitigating Source Bias for Fairer Weak Supervision

Changho Shin, Sonia Cromp, Dyah Adila, Frederic Sala

ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers

Pascal Notin, Ruben Weitzman, Debora Marks, Yarin Gal

Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation

Wengong Jin, Siranush Sarkizova, Xun Chen, Nir HaCohen, Caroline Uhler

Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework

Weiduo Liao, Ying Wei, Mingchen Jiang, Qingfu Zhang, Hisao Ishibuchi

EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding

Shuhan Tan, Tushar Nagarajan, Kristen Grauman

PIXIU: A Comprehensive Benchmark, Instruction Dataset and Large Language Model for Finance

Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng

Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations

Yu Cao, Jingrun Chen, Yixin Luo, Xiang Zhou

Knowledge Distillation for High Dimensional Search Index

Zepu Lu, Jin Chen, Defu Lian, ZAIXI ZHANG, Yong Ge

Rethinking Gauss-Newton for learning over-parameterized models

Michael Arbel, Romain Menegaux, Pierre Wolinski

A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space

Wenchong He, Zhe Jiang, Tingsong Xiao, Zelin Xu, Shigang Chen

Combinatorial Group Testing with Selfish Agents

Georgios Chionas, Dariusz Kowalski, Piotr Krysta

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure

Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu

ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer

Haoran You, Huihong Shi, Yipin Guo, Yingyan Lin

Nash Regret Guarantees for Linear Bandits

Ayush Sawarni, Soumyabrata Pal, Siddharth Barman

Online Corrupted User Detection and Regret Minimization

Zhiyong Wang, Jize Xie, Tong Yu, Shuai Li, John C.S. Lui

Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions

Kai Liu, Zhihang Fu, Chao Chen, Sheng Jin, Ze Chen

Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models

Chirag Raman, Alec Nonnemaker, Amelia Villegas-Morcillo, Hayley Hung, Marco Loog

Hierarchically Gated Recurrent Neural Network for Sequence Modeling

Zhen Qin, Songlin Yang, Yiran Zhong

On the Pareto Front of Multilingual Neural Machine Translation

Liang Chen, Shuming Ma, Dongdong Zhang, Furu Wei, Baobao Chang