Jin, Chi

79 publications

ICLR 2025 Benign Overfitting in Out-of-Distribution Generalization of Linear Models Shange Tang, Jiayun Wu, Jianqing Fan, Chi Jin

ICLR 2025 Building Math Agents with Multi-Turn Iterative Preference Learning Wei Xiong, Chengshuai Shi, Jiaming Shen, Aviv Rosenberg, Zhen Qin, Daniele Calandriello, Misha Khalman, Rishabh Joshi, Bilal Piot, Mohammad Saleh, Chi Jin, Tong Zhang, Tianqi Liu

ICCV 2025 DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization Zihan Ding, Chi Jin, Difan Liu, Haitian Zheng, Krishna Kumar Singh, Qiang Zhang, Yan Kang, Zhe Lin, Yuchen Liu

NeurIPS 2025 Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving of Inequalities Haoyu Zhao, Yihan Geng, Shange Tang, Yong Lin, Bohan Lyu, Hongzhou Lin, Chi Jin, Sanjeev Arora

NeurIPS 2025 Learning World Models for Interactive Video Generation Taiye Chen, Xun Hu, Zihan Ding, Chi Jin

ICLRW 2025 MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities Against Hard Perturbations Kaixuan Huang, Jiacheng Guo, Zihao Li, Xiang Ji, Jiawei Ge, Wenzhe Li, Yingqing Guo, Tianle Cai, Hui Yuan, Runzhe Wang, Yue Wu, Ming Yin, Shange Tang, Yangsibo Huang, Chi Jin, Xinyun Chen, Chiyuan Zhang, Mengdi Wang

ICML 2025 MATH-Perturb: Benchmarking LLMs’ Math Reasoning Abilities Against Hard Perturbations Kaixuan Huang, Jiacheng Guo, Zihao Li, Xiang Ji, Jiawei Ge, Wenzhe Li, Yingqing Guo, Tianle Cai, Hui Yuan, Runzhe Wang, Yue Wu, Ming Yin, Shange Tang, Yangsibo Huang, Chi Jin, Xinyun Chen, Chiyuan Zhang, Mengdi Wang

ICML 2025 PokéChamp: An Expert-Level Minimax Language Agent Seth Karten, Andy Luu Nguyen, Chi Jin

ICML 2025 Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games Jiawei Ge, Yuanhao Wang, Wenzhe Li, Chi Jin

JMLR 2025 Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization Tianyi Lin, Chi Jin, Michael I. Jordan

NeurIPS 2025 Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration Ahmed Khaled, Satyen Kale, Arthur Douillard, Chi Jin, Rob Fergus, Manzil Zaheer

NeurIPSW 2024 Benign Overfitting in Out-of-Distribution Generalization of Linear Models Shange Tang, Jiayun Wu, Jianqing Fan, Chi Jin

ICLR 2024 Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning Zihan Ding, Chi Jin

ICML 2024 FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin

ICLRW 2024 FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin

ICLR 2024 Maximum Likelihood Estimation Is All You Need for Well-Specified Covariate Shift Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin

ICLR 2024 On the Provable Advantage of Unsupervised Pretraining Jiawei Ge, Shange Tang, Jianqing Fan, Chi Jin

NeurIPSW 2024 PokéChamp: An Expert-Level Minimax Language Agent for Competitive Pokémon Seth Karten, Andy Luu Nguyen, Chi Jin

NeurIPSW 2024 Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Wenzhe Li, Yong Lin, Mengzhou Xia, Chi Jin

ICML 2024 Tuning-Free Stochastic Optimization Ahmed Khaled, Chi Jin

COLT 2023 Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin

NeurIPS 2023 Context-Lumpable Stochastic Bandits Chung-Wei Lee, Qinghua Liu, Yasin Abbasi Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvari

NeurIPS 2023 DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method Ahmed Khaled, Konstantin Mishchenko, Chi Jin

ICML 2023 Efficient Displacement Convex Optimization with Particle Gradient Descent Hadi Daneshmand, Jason D. Lee, Chi Jin

ICLR 2023 Faster Federated Optimization Under Second-Order Similarity Ahmed Khaled, Chi Jin

NeurIPS 2023 Is RLHF More Difficult than Standard RL? a Theoretical Perspective Yuanhao Wang, Qinghua Liu, Chi Jin

ICLR 2023 Learning Rationalizable Equilibria in Multiplayer Games Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin

NeurIPSW 2023 Maximum Likelihood Estimation Is All You Need for Well-Specified Covariate Shift Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin

NeurIPS 2023 Optimistic Natural Policy Gradient: A Simple Efficient Policy Optimization Framework for Online RL Qinghua Liu, Gellert Weisz, András György, Chi Jin, Csaba Szepesvari

ICLR 2023 Provable Sim-to-Real Transfer in Continuous Domain with Partial Observations Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang

ICLR 2023 Representation Learning for Low-Rank General-Sum Markov Games Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang

ICML 2022 A Simple Reward-Free Approach to Constrained Reinforcement Learning Sobhan Miryoosefi, Chi Jin

NeurIPS 2022 Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu

ICML 2022 Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits Qinghua Liu, Yuanhao Wang, Chi Jin

ICLR 2022 Minimax Optimization with Smooth Algorithmic Adversaries Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J Ratliff

ICML 2022 Near-Optimal Learning of Extensive-Form Games with Imperfect Information Yu Bai, Chi Jin, Song Mei, Tiancheng Yu

ICLRW 2022 Near-Optimal Learning of Extensive-Form Games with Imperfect Information Yu Bai, Chi Jin, Song Mei, Tiancheng Yu

ICML 2022 Provable Reinforcement Learning with a Short-Term Memory Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi

NeurIPS 2022 Sample-Efficient Reinforcement Learning of Partially Observable Markov Games Qinghua Liu, Csaba Szepesvari, Chi Jin

ICML 2022 The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces Chi Jin, Qinghua Liu, Tiancheng Yu

ICLR 2022 Understanding Domain Randomization for Sim-to-Real Transfer Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang

ICLRW 2022 V-Learning -- a Simple, Efficient, Decentralized Algorithm for Multiagent RL Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu

COLT 2022 When Is Partially Observable Reinforcement Learning Not Scary? Qinghua Liu, Alan Chung, Csaba Szepesvari, Chi Jin

COLT 2021 A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network Mo Zhou, Rong Ge, Chi Jin

ICML 2021 A Sharp Analysis of Model-Based Reinforcement Learning with Self-Play Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin

NeurIPS 2021 Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms Chi Jin, Qinghua Liu, Sobhan Miryoosefi

ICML 2021 Near-Optimal Representation Learning for Linear Bandits and Linear RL Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang

ICML 2021 Provable Meta-Learning of Linear Representations Nilesh Tripuraneni, Chi Jin, Michael Jordan

ICLR 2021 Provable Rich Observation Reinforcement Learning with Combinatorial Latent States Dipendra Misra, Qinghua Liu, Chi Jin, John Langford

ICML 2021 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning Yaqi Duan, Chi Jin, Zhiyuan Li

NeurIPS 2021 Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games Yu Bai, Chi Jin, Huan Wang, Caiming Xiong

ICML 2020 Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu

COLT 2020 Near-Optimal Algorithms for Minimax Optimization Tianyi Lin, Chi Jin, Michael I. Jordan

NeurIPS 2020 Near-Optimal Reinforcement Learning with Self-Play Yu Bai, Chi Jin, Tiancheng Yu

ICML 2020 On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems Tianyi Lin, Chi Jin, Michael Jordan

NeurIPS 2020 On the Theory of Transfer Learning: The Importance of Task Diversity Nilesh Tripuraneni, Michael I. Jordan, Chi Jin

ICML 2020 Provable Self-Play Algorithms for Competitive Reinforcement Learning Yu Bai, Chi Jin

ICML 2020 Provably Efficient Exploration in Policy Optimization Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang

NeurIPS 2020 Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan

COLT 2020 Provably Efficient Reinforcement Learning with Linear Function Approximation Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I Jordan

ICML 2020 Reward-Free Exploration for Reinforcement Learning Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu

NeurIPS 2020 Sample-Efficient Reinforcement Learning of Undercomplete POMDPs Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu

ICML 2020 What Is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? Chi Jin, Praneeth Netrapalli, Michael Jordan

COLT 2018 Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent Chi Jin, Praneeth Netrapalli, Michael I. Jordan

NeurIPS 2018 Is Q-Learning Provably Efficient? Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I Jordan

NeurIPS 2018 On the Local Minima of the Empirical Risk Chi Jin, Lydia T. Liu, Rong Ge, Michael I Jordan

NeurIPS 2018 Stochastic Cubic Regularization for Fast Nonconvex Optimization Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I Jordan

AISTATS 2017 Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli

NeurIPS 2017 Gradient Descent Can Take Exponential Time to Escape Saddle Points Simon S Du, Chi Jin, Jason Lee, Michael I Jordan, Aarti Singh, Barnabas Poczos

ICML 2017 How to Escape Saddle Points Efficiently Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan

ICML 2017 No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis Rong Ge, Chi Jin, Yi Zheng

JMLR 2016 Differentially Private Data Releasing for Smooth Queries Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang

ICML 2016 Efficient Algorithms for Large-Scale Generalized Eigenvector Computation and Canonical Correlation Analysis Rong Ge, Chi Jin, Sham, Praneeth Netrapalli, Aaron Sidford

ICML 2016 Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford

NeurIPS 2016 Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I Jordan

NeurIPS 2016 Provable Efficient Online Matrix Completion via Non-Convex Stochastic Gradient Descent Chi Jin, Sham M. Kakade, Praneeth Netrapalli

COLT 2016 Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford

COLT 2015 Escaping from Saddle Points - Online Stochastic Gradient for Tensor Decomposition Rong Ge, Furong Huang, Chi Jin, Yang Yuan

NeurIPS 2012 Dimensionality Dependent PAC-Bayes Margin Bound Chi Jin, Liwei Wang