Dimensionality Dependent PAC-Bayes Margin Bound. - Chi Jin 001; Liwei Wang 001 (NIPS, 2012)

Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output. - Chi Jin 001; Ziteng Wang; Junliang Huang; Yiqiao Zhong; Liwei Wang 001 (CoRR, 2014)

Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. - Rong Ge 001; Furong Huang; Chi Jin 001; Yang Yuan (COLT, 2015)

Computing Matrix Squareroot via Non Convex Local Search. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (CoRR, 2015)

Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. - Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (CoRR, 2015)

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (COLT, 2016)

Faster Eigenvector Computation via Shift-and-Invert Preconditioning. - Dan Garber; Elad Hazan; Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. - Rong Ge 001; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. - Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (NIPS, 2016)

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. - Chi Jin 001; Yuchen Zhang 002; Sivaraman Balakrishnan; Martin J. Wainwright; Michael I. Jordan (NIPS, 2016)

Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

Differentially Private Data Releasing for Smooth Queries. - Ziteng Wang; Chi Jin 001; Kai Fan; Jiaqi Zhang; Junliang Huang; Yiqiao Zhong; Liwei Wang 001 (J. Mach. Learn. Res., 2016)

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli (AISTATS, 2017)

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. - Rong Ge 001; Chi Jin 001; Yi Zheng (ICML, 2017)

How to Escape Saddle Points Efficiently. - Chi Jin 001; Rong Ge 001; Praneeth Netrapalli; Sham M. Kakade; Michael I. Jordan (ICML, 2017)

Gradient Descent Can Take Exponential Time to Escape Saddle Points. - Simon S. Du; Chi Jin 001; Jason D. Lee; Michael I. Jordan; Aarti Singh; Barnabás Póczos (NIPS, 2017)

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. - Chi Jin 001; Praneeth Netrapalli; Michael I. Jordan (COLT, 2018)

Is Q-Learning Provably Efficient? - Chi Jin 001; Zeyuan Allen-Zhu; Sébastien Bubeck; Michael I. Jordan (NeurIPS, 2018)

On the Local Minima of the Empirical Risk. - Chi Jin 001; Lydia T. Liu; Rong Ge 001; Michael I. Jordan (NeurIPS, 2018)

Stochastic Cubic Regularization for Fast Nonconvex Optimization. - Nilesh Tripuraneni; Mitchell Stern; Chi Jin 001; Jeffrey Regier; Michael I. Jordan (NeurIPS, 2018)

Minimizing Nonconvex Population Risk from Rough Empirical Risk. - Chi Jin 001; Lydia T. Liu; Rong Ge 001; Michael I. Jordan (CoRR, 2018)

Stability and Convergence Trade-off of Iterative Optimization Algorithms. - Yuansi Chen; Chi Jin 001; Bin Yu 001 (CoRR, 2018)

Sampling Can Be Faster Than Optimization. - Yi-An Ma; Yuansi Chen; Chi Jin 001; Nicolas Flammarion; Michael I. Jordan (CoRR, 2018)

Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. - Chi Jin 001; Praneeth Netrapalli; Michael I. Jordan (CoRR, 2019)

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

Stochastic Gradient Descent Escapes Saddle Points Efficiently. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

Machine Learning: Why Do Simple Algorithms Work So Well? - Chi Jin 001 (2019)

Near-Optimal Algorithms for Minimax Optimization. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (COLT, 2020)

Provable Self-Play Algorithms for Competitive Reinforcement Learning. - Yu Bai 017; Chi Jin 001 (ICML, 2020)

Provably Efficient Exploration in Policy Optimization. - Qi Cai; Zhuoran Yang; Chi Jin 001; Zhaoran Wang 001 (ICML, 2020)

Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. - Chi Jin 001; Tiancheng Jin; Haipeng Luo; Suvrit Sra; Tiancheng Yu (ICML, 2020)

Reward-Free Exploration for Reinforcement Learning. - Chi Jin 001; Akshay Krishnamurthy; Max Simchowitz; Tiancheng Yu (ICML, 2020)

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? - Chi Jin 001; Praneeth Netrapalli; Michael I. Jordan (ICML, 2020)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (ICML, 2020)

Near-Optimal Reinforcement Learning with Self-Play. - Yu Bai 017; Chi Jin 001; Tiancheng Yu (NeurIPS, 2020)

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. - Chi Jin 001; Sham M. Kakade; Akshay Krishnamurthy; Qinghua Liu (NeurIPS, 2020)

On the Theory of Transfer Learning: The Importance of Task Diversity. - Nilesh Tripuraneni; Michael I. Jordan; Chi Jin 001 (NeurIPS, 2020)

Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. - Zhuoran Yang; Chi Jin 001; Zhaoran Wang 001; Mengdi Wang; Michael I. Jordan (NeurIPS, 2020)

Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. - Zhuoran Yang; Chi Jin 001; Zhaoran Wang 001; Mengdi Wang; Michael I. Jordan (CoRR, 2020)

A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. - Mo Zhou; Rong Ge 001; Chi Jin 001 (COLT, 2021)

Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. - Dipendra Misra; Qinghua Liu; Chi Jin 001; John Langford 001 (ICLR, 2021)

Near-Optimal Representation Learning for Linear Bandits and Linear RL. - Jiachen Hu; Xiaoyu Chen 008; Chi Jin 001; Lihong Li 001; Liwei Wang 001 (ICML, 2021)

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. - Qinghua Liu; Tiancheng Yu; Yu Bai 017; Chi Jin 001 (ICML, 2021)

Provable Meta-Learning of Linear Representations. - Nilesh Tripuraneni; Chi Jin 001; Michael I. Jordan (ICML, 2021)

Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. - Yu Bai 017; Chi Jin 001; Huan Wang 014; Caiming Xiong (NeurIPS, 2021)

Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms. - Chi Jin 001; Qinghua Liu; Sobhan Miryoosefi (NeurIPS, 2021)

V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent RL. - Chi Jin 001; Qinghua Liu; Yuanhao Wang 001; Tiancheng Yu (CoRR, 2021)

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (J. ACM, 2021)

When Is Partially Observable Reinforcement Learning Not Scary? - Qinghua Liu; Alan Chung; Csaba Szepesvári; Chi Jin 001 (COLT, 2022)

Understanding Domain Randomization for Sim-to-real Transfer. - Xiaoyu Chen 008; Jiachen Hu; Chi Jin 001; Lihong Li 001; Liwei Wang 001 (ICLR, 2022)

Minimax Optimization with Smooth Algorithmic Adversaries. - Tanner Fiez; Chi Jin 001; Praneeth Netrapalli; Lillian J. Ratliff (ICLR, 2022)

Near-Optimal Learning of Extensive-Form Games with Imperfect Information. - Yu Bai 017; Chi Jin 001; Song Mei; Tiancheng Yu (ICML, 2022)

Provable Reinforcement Learning with a Short-Term Memory. - Yonathan Efroni; Chi Jin 001; Akshay Krishnamurthy; Sobhan Miryoosefi (ICML, 2022)

The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. - Chi Jin 001; Qinghua Liu; Tiancheng Yu (ICML, 2022)

Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. - Qinghua Liu; Yuanhao Wang 001; Chi Jin 001 (ICML, 2022)

A Simple Reward-free Approach to Constrained Reinforcement Learning. - Sobhan Miryoosefi; Chi Jin 001 (ICML, 2022)

Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent. - Yu Bai 017; Chi Jin 001; Song Mei; Ziang Song; Tiancheng Yu (NeurIPS, 2022)

Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. - Qinghua Liu; Csaba Szepesvári; Chi Jin 001 (NeurIPS, 2022)

Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. - Yu Bai 017; Chi Jin 001; Song Mei; Ziang Song; Tiancheng Yu (CoRR, 2022)

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games. - Zihan Ding; Dijia Su; Qinghua Liu; Chi Jin 001 (CoRR, 2022)

Representation Learning for General-sum Low-rank Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Chi Jin 001; Mengdi Wang (CoRR, 2022)

Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. - Yuanhao Wang 001; Qinghua Liu; Yu Bai 017; Chi Jin 001 (COLT, 2023)

Faster federated optimization under second-order similarity. - Ahmed Khaled 001; Chi Jin 001 (ICLR, 2023)

Learning Rationalizable Equilibria in Multiplayer Games. - Yuanhao Wang 001; Dingwen Kong; Yu Bai 017; Chi Jin 001 (ICLR, 2023)

Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. - Jiachen Hu; Han Zhong 001; Chi Jin 001; Liwei Wang 001 (ICLR, 2023)

Representation Learning for Low-rank General-sum Markov Games. - Chengzhuo Ni; Yuda Song 001; Xuezhou Zhang; Zihan Ding; Chi Jin 001; Mengdi Wang (ICLR, 2023)

Efficient displacement convex optimization with particle gradient descent. - Hadi Daneshmand; Jason D. Lee; Chi Jin 001 (ICML, 2023)

Is RLHF More Difficult than Standard RL? A Theoretical Perspective. - Yuanhao Wang 001; Qinghua Liu; Chi Jin 001 (NeurIPS, 2023)

DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. - Ahmed Khaled 001; Konstantin Mishchenko; Chi Jin 001 (NeurIPS, 2023)

Context-lumpable stochastic bandits. - Chung-Wei Lee; Qinghua Liu; Yasin Abbasi-Yadkori; Chi Jin 001; Tor Lattimore; Csaba Szepesvári (NeurIPS, 2023)

Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. - Qinghua Liu; Gellért Weisz; András György 001; Chi Jin 001; Csaba Szepesvári (NeurIPS, 2023)

Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. - Qinghua Liu; Praneeth Netrapalli; Csaba Szepesvári; Chi Jin 001 (STOC, 2023)

Learning a Universal Human Prior for Dexterous Manipulation from Human Preference. - Zihan Ding; Yuanpei Chen; Allen Z. Ren; Shixiang Shane Gu; Hao Dong 003; Chi Jin 001 (CoRR, 2023)

Is RLHF More Difficult than Standard RL? - Yuanhao Wang 001; Qinghua Liu; Chi Jin 001 (CoRR, 2023)

ZeroSwap: Data-driven Optimal Market Making in DeFi. - Viraj Nadkarni; Jiachen Hu; Ranvir Rana; Chi Jin 001; Sanjeev R. Kulkarni; Pramod Viswanath (CoRR, 2023)

Provably Efficient Reinforcement Learning with Linear Function Approximation. - Chi Jin 001; Zhuoran Yang; Zhaoran Wang 001; Michael I. Jordan (Math. Oper. Res., 2023)

Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol. - Mahsa Bastankhah; Viraj Nadkarni; Chi Jin 001; Sanjeev Kulkarni 001; Pramod Viswanath (AFT, 2024)

ZeroSwap: Data-Driven Optimal Market Making in Decentralized Finance. - Viraj Nadkarni; Jiachen Hu; Ranvir Rana; Chi Jin 001; Sanjeev Kulkarni 001; Pramod Viswanath (FC (1), 2024)

Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning. - Zihan Ding; Chi Jin 001 (ICLR, 2024)

On the Provable Advantage of Unsupervised Pretraining. - Jiawei Ge 003; Shange Tang; Jianqing Fan; Chi Jin 001 (ICLR, 2024)

Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift. - Jiawei Ge 003; Shange Tang; Jianqing Fan; Cong Ma 001; Chi Jin 001 (ICLR, 2024)

Tuning-Free Stochastic Optimization. - Ahmed Khaled 001; Chi Jin 001 (ICML, 2024)

FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning. - Wenzhe Li; Zihan Ding; Seth Karten; Chi Jin 001 (ICML, 2024)

On Limitation of Transformer for Learning HMMs. - Jiachen Hu; Qinghua Liu; Chi Jin 001 (CoRR, 2024)

Towards Principled Superhuman AI for Multiplayer Symmetric Games. - Jiawei Ge 003; Yuanhao Wang 001; Wenzhe Li; Chi Jin 001 (CoRR, 2024)

Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol. - Mahsa Bastankhah; Viraj Nadkarni; Xuechao Wang; Chi Jin 001; Sanjeev Kulkarni 001; Pramod Viswanath (CoRR, 2024)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. - Tianyi Lin; Chi Jin 001; Michael I. Jordan (CoRR, 2024)

Generative Diffusion Modeling: A Practical Handbook. - Zihan Ding; Chi Jin 001 (CoRR, 2024)

V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent Reinforcement Learning. - Chi Jin 001; Qinghua Liu; Yuanhao Wang 001; Tiancheng Yu (Math. Oper. Res., 2024)

Benign Overfitting in Out-of-Distribution Generalization of Linear Models. - Shange Tang; Jiayun Wu; Jianqing Fan; Chi Jin 001 (ICLR, 2025)

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. - Kaixuan Huang; Jiacheng Guo; Zihao Li; Xiang Ji; Jiawei Ge 003; Wenzhe Li; Yingqing Guo; Tianle Cai; Hui Yuan 002; Runzhe Wang; Yue Wu; Ming Yin; et al. (CoRR, 2025)

Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving. - Yong Lin; Shange Tang; Bohan Lyu 001; Jiayun Wu; Hongzhou Lin; Kaiyu Yang; Jia Li; Mengzhou Xia; Danqi Chen 001; Sanjeev Arora; Chi Jin 001 (CoRR, 2025)

Is Elo Rating Reliable? A Study Under Model Misspecification. - Shange Tang; Yuanhao Wang 001; Chi Jin 001 (CoRR, 2025)

PokéChamp: an Expert-level Minimax Language Agent. - Seth Karten; Andy Luu Nguyen; Chi Jin 001 (CoRR, 2025)

Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving on Inequalities. - Haoyu Zhao; Yihan Geng; Shange Tang; Yong Lin; Bohan Lyu 001; Hongzhou Lin; Chi Jin 001; Sanjeev Arora (CoRR, 2025)

Learning World Models for Interactive Video Generation. - Taiye Chen; Xun Hu; Zihan Ding; Chi Jin 001 (CoRR, 2025)

Principled Out-of-Distribution Generalization via Simplicity. - Jiawei Ge 003; Amanda Wang; Shange Tang; Chi Jin 001 (CoRR, 2025)

Frontier LLMs Still Struggle with Simple Reasoning Tasks. - Alan Malek; Jiawei Ge 003; Nevena Lazic; Chi Jin 001; András György 001; Csaba Szepesvári (CoRR, 2025)

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra. - Seth Karten; Wenzhe Li; Zihan Ding; Samuel Kleiner; Yu Bai 017; Chi Jin 001 (CoRR, 2025)

Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction. - Yong Lin; Shange Tang; Bohan Lyu 001; Ziran Yang; Jui-Hui Chung; Haoyu Zhao; Lai Jiang; Yihan Geng; Jiawei Ge 003; Jingruo Sun; Jiayun Wu; Jiri Gesi; et al. (CoRR, 2025)