ICML 2023

1,828 papers

GREAD: Graph Neural Reaction-Diffusion Networks

Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho

On the Convergence of Federated Averaging with Cyclic Client Participation

Yae Jee Cho, PRANAY SHARMA, Gauri Joshi, Zheng Xu, Satyen Kale

Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data

Cheol Jun Cho, Edward Chang, Gopala Anumanchipalli

Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations

Hong-Ming Chiu, Richard Y. Zhang

PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation

Eli Chien, Jiong Zhang, Cho-jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang

Hiding Data Helps: On the Benefits of Masking for Sparse Coding

Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup

Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge

Tighter Bounds on the Expressivity of Transformer Encoders

David Chiang, Peter Cholak, Anand Pillay

Understanding the Role of Feedback in Online Learning with Switching Costs

Duo Cheng, Xingyu Zhou, Bo Ji

Mu$^2$SLAM: Multitask, Multilingual Speech and Language Models

Yong Cheng, Yu Zhang, Melvin Johnson, Wolfgang Macherey, Ankur Bapna

Parallel Online Clustering of Bandits via Hedonic Game

Xiaotong Cheng, Cheng Pan, Setareh Maghsudi

Identification of the Adversary from a Single Adversarial Example

Minhao Cheng, Rui Min, Haochen Sun, Pin-Yu Chen

PLay: Parametrically Conditioned Layout Generation using Latent Diffusion

Chin-Yi Cheng, Forrest Huang, Gang Li, Yang Li

Weakly Supervised Regression with Interval Targets

Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng

Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization

Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu

Lifelong Language Pretraining with Distribution-Specialized Experts

Wuyang Chen, Yanqi Zhou, Nan Du, Yanping Huang, James Laudon

Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling

Tianqi Chen, Mingyuan Zhou

Bidirectional Learning for Offline Model-based Biological Sequence Design

Can Chen, Yingxue Zhang, Xue Liu, Mark Coates

Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

Dingyang Chen, Qi Zhang

Sketched Ridgeless Linear Regression: The Role of Downsampling

Xin Chen, Yicheng Zeng, Siyue Yang, Qiang Sun

How to address monotonicity for model risk management?

Dangxing Chen, Weicheng Ye

A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening

Yifan Chen, Rentian Yao, Yun Yang, Jie Chen

Efficient Personalized Federated Learning via Sparse Model-Adaptation

Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li

Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization

Lesi Chen, Jing Xu, Luo Luo

Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model

Siyu Chen, Jibang Wu, Yifan Wu, Zhuoran Yang

BEATs: Audio Pre-Training with Acoustic Tokenizers

Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie LIU, Daniel Tompkins

Implicit Neural Spatial Representations for Time-dependent PDEs

Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen

Lower Bounds for Learning in Revealing POMDPs

Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai

Semi-Offline Reinforcement Learning for Optimized Text Generation

Changyu CHEN, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong

Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen

Learning to Optimize Differentiable Games

Xuxi Chen, Nelson Vadori, Tianlong Chen, Zhangyang Wang

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization

Sijia Chen, Wei-Wei Tu, Peng Zhao, Lijun Zhang

Layered State Discovery for Incremental Autonomous Exploration

Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta

Model Transferability with Responsive Decision Subjects

Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu

Multi-task Hierarchical Adversarial Inverse Reinforcement Learning

Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal

Stratified Adversarial Robustness with Rejection

Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang

Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues

Jiaxuan Chen, Yu Qi, Gang Pan

Fisher Information Embedding for Node and Graph Learning

Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt

Generalized Implicit Follow-The-Regularized-Leader

Keyi Chen, Francesco Orabona

HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation

Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang

Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers

Yineng Chen, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao

Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions

Hongrui Chen, Holden Lee, Jianfeng Lu

Sample Complexity of Probability Divergences under Group Symmetry

Ziyu Chen, Markos Katsoulakis, Luc Rey-Bellet, Wei Zhu

Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data

Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang

Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity

Xuxing Chen, Minhui Huang, Shiqian Ma, Krishna Balasubramanian

Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization

Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, Zhi-ming Ma

Evolving Semantic Prototype Improves Generative Zero-Shot Learning

Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song

Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling

Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu

GuardHFL: Privacy Guardian for Heterogeneous Federated Learning

Hanxiao Chen, Meng Hao, Hongwei Li, Kangjie Chen, Guowen Xu

Subequivariant Graph Reinforcement Learning in 3D Environments

Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang

Is Learning Summary Statistics Necessary for Likelihood-free Inference?

Yanzhi Chen, Michael U. Gutmann, Adrian Weller

ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines

Siyuan Chen, Pratik Pramod Fegade, Tianqi Chen, Phillip Gibbons, Todd Mowry

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation

Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang

Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers

Sitan Chen, Giannis Daras, Alex Dimakis

Differentially Private Stochastic Convex Optimization under a Quantile Loss Function

Du Chen, Geoffrey A. Chua

Trompt: Towards a Better Deep Neural Network for Tabular Data

Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Ting-Wei Chen, Tien-Hao Chang

Beyond the Edge of Stability via Two-step Gradient Updates

Lei Chen, Joan Bruna

Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces

Zhengdao Chen

On the Statistical Benefits of Temporal Difference Learning

David Cheikhi, Daniel Russo

Fast Federated Machine Unlearning with Nonlinear Functional Theory

Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu

Correcting discount-factor mismatch in on-policy policy gradient methods

Fengdi Che, Gautham Vasan, A. Rupam Mahmood

Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits

Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant

Why does Throwing Away Data Improve Worst-Group Error?

Kamalika Chaudhuri, Kartik Ahuja, Martin Arjovsky, David Lopez-paz

Streaming Submodular Maximization with Differential Privacy

Anamay Chaturvedi, Huy Nguyen, Thy Dinh Nguyen

Robust and private stochastic linear bandits

Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni

On Investigating the Conservative Property of Score-Based Generative Models

Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee

Muse: Text-To-Image Generation via Masked Generative Transformers

Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama

Memory-Based Dual Gaussian Processes for Sequential Learning

Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition

Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos

Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits

Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari

STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning

Souradip Chakraborty, Amrit Bedi, Alec Koppel, Mengdi Wang, Furong Huang

Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations

Jaehoon Cha, Jeyan Thiyagalingam

Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond

Jaeyoung Cha, Jaewook Lee, Chulhee Yun

Learning Globally Smooth Functions on Manifolds

Juan Cerviño, Luiz F. O. Chamon, Benjamin David Haeffele, René Vidal, Alejandro Ribeiro

On the Robustness of Text Vectorizers

Rémi Catellier, Samuel Vaiter, Damien Garreau

LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning

Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo

Scalable Safe Policy Improvement via Monte Carlo Tree Search

Alberto Castellini, Federico Bianchi, Edoardo Zorzi, Thiago D. Simão, Alessandro Farinelli

Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning

Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier

Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning

Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud

State and parameter learning with PARIS particle Gibbs

Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson

One-sided Matrix Completion from Two Observations Per Row

Steven Cao, Percy Liang, Gregory Valiant

Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation

Shengcao Cao, Mengtian Li, James Hays, Deva Ramanan, Yu-Xiong Wang

Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization

Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Tobias Schaefer

Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network

Yadi Cao, Menglei Chai, Minchen Li, Chenfanfu Jiang

Multi-Agent Learning from Learners

Mine Melodi Caliskan, Francesco Chini, Setareh Maghsudi

Doubly Optimal No-Regret Learning in Monotone Games

Yang Cai, Weiqiang Zheng

Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?

Ruisi Cai, Zhenyu Zhang, Zhangyang Wang

Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization

Xufeng Cai, Chaobing Song, Stephen Wright, Jelena Diakonikolas

Extrapolated Random Tree for Regression

Yuchao Cai, Yuheng Ma, Yiwei Dong, Hanfang Yang

Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition

Dongqi Cai, Yangyuxuan Kang, Anbang Yao, Yurong Chen

On the Connection Between MPNN and Graph Transformer

Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang

Causal Discovery with Latent Confounders Based on Higher-Order Cumulants

Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang

What Can Be Learnt With Wide Convolutional Neural Networks?

Francesco Cagnetta, Alessandro Favero, Matthieu Wyart

MyoDex: A Generalizable Prior for Dexterous Manipulation

Vittorio Caggiano, Sudeep Dasari, Vikash Kumar

Online Mechanism Design for Information Acquisition

Federico Cacciamani, Matteo Castiglioni, Nicola Gatti

The SSL Interplay: Augmentations, Inductive Bias, and Generalization

Vivien Cabannes, Bobak Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti

Label differential privacy and private training data release

Róbert István Busa-Fekete, Andres Munoz Medina, Umar Syed, Sergei Vassilvitskii

Machine Learning Force Fields with Data Cost Aware Training

Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao

Differentially Private Optimization on Large Model at Small Cost

Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

Emergence of Sparse Representations from Noise

Trenton Bricken, Rylan Schaeffer, Bruno Olshausen, Gabriel Kreiman