Chi Jin

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 126

Top Venues

Publications

Mahsa Bastankhah, Viraj Nadkarni, Xuechao Wang, Chi Jin, Sanjeev Kulkarni, Pramod Viswanath
Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol. CoRR (2024)
Ahmed Khaled, Chi Jin
Tuning-Free Stochastic Optimization. CoRR (2024)
Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift. CoRR (2023)
Viraj Nadkarni, Jiachen Hu, Ranvir Rana, Chi Jin, Sanjeev R. Kulkarni, Pramod Viswanath
ZeroSwap: Data-driven Optimal Market Making in DeFi. CoRR (2023)
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
Context-lumpable stochastic bandits. CoRR (2023)
Jiawei Ge, Shange Tang, Jianqing Fan, Chi Jin
On the Provable Advantage of Unsupervised Pretraining. CoRR (2023)
Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. ICLR (2023)
Ahmed Khaled, Konstantin Mishchenko, Chi Jin
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. CoRR (2023)
Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang
Representation Learning for Low-rank General-sum Markov Games. ICLR (2023)
Bowen Yi, Chi Jin, Lei Wang, Guodong Shi, Viorela Ila, Ian R. Manchester
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees. CoRR (2023)
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
Context-lumpable stochastic bandits. NeurIPS (2023)
Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. CoRR (2022)
Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. CoRR (2022)
Bowen Yi, Chi Jin, Ian R. Manchester
Globally convergent visual-feature range estimation with biased inertial measurements. Autom. 146 (2022)
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
Optimistic MLE - A Generic Model-based Algorithm for Partially Observable Sequential Decision Making. CoRR (2022)
Qinghua Liu, Yuanhao Wang, Chi Jin
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. CoRR (2022)
Jin Wu, Ming Liu, Yulong Huang, Chi Jin, Yuanxin Wu, Changbin Yu
SE(n)++: An Efficient Solution to Multiple Pose Estimation Problems. IEEE Trans. Cybern. 52 (5) (2022)
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
When Is Partially Observable Reinforcement Learning Not Scary? COLT (2022)
Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi
Provable Reinforcement Learning with a Short-Term Memory. CoRR (2022)
Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu
Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. CoRR (2022)
Zihan Ding, Dijia Su, Qinghua Liu, Chi Jin
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games. CoRR (2022)
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
When Is Partially Observable Reinforcement Learning Not Scary? CoRR (2022)
Ahmed Khaled, Chi Jin
Faster federated optimization under second-order similarity. CoRR (2022)
Sobhan Miryoosefi, Chi Jin
A Simple Reward-free Approach to Constrained Reinforcement Learning. ICML (2022)
Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
Minimax Optimization with Smooth Algorithmic Adversaries. ICLR (2022)
Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. ICML (2022)
Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, Mengdi Wang
Representation Learning for General-sum Low-rank Markov Games. CoRR (2022)
Qinghua Liu, Csaba Szepesvári, Chi Jin
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. CoRR (2022)
Qinghua Liu, Yuanhao Wang, Chi Jin
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. ICML (2022)
Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin
Learning Rationalizable Equilibria in Multiplayer Games. CoRR (2022)
Chi Jin, Qinghua Liu, Tiancheng Yu
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. ICML (2022)
Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi
Provable Reinforcement Learning with a Short-Term Memory. ICML (2022)
Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang
Understanding Domain Randomization for Sim-to-real Transfer. ICLR (2022)
Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang
Understanding Domain Randomization for Sim-to-real Transfer. CoRR (2021)
Yaqi Duan, Chi Jin, Zhiyuan Li
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning. CoRR (2021)
Bowen Yi, Chi Jin, Ian R. Manchester
Globally convergent visual-feature range estimation with biased inertial measurements. CoRR (2021)
Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. NeurIPS (2021)
Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
Minimax Optimization with Smooth Algorithmic Adversaries. CoRR (2021)
Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. CoRR (2021)
Yaqi Duan, Chi Jin, Zhiyuan Li
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning. ICML (2021)
Mo Zhou, Rong Ge, Chi Jin
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. CoRR (2021)
Chi Jin, Qinghua Liu, Tiancheng Yu
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. CoRR (2021)
Mo Zhou, Rong Ge, Chi Jin
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. COLT (2021)
Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu
V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent RL. CoRR (2021)
Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
Provable Meta-Learning of Linear Representations. ICML (2021)
Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang
Near-optimal Representation Learning for Linear Bandits and Linear RL. CoRR (2021)
Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang
Near-Optimal Representation Learning for Linear Bandits and Linear RL. ICML (2021)
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM 68 (2) (2021)
Chi Jin, Qinghua Liu, Sobhan Miryoosefi
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms. CoRR (2021)
Zhe Zhang, Chi Jin, Yi Tang, Chaoyu Dong, Pengfeng Lin, Yang Mi, Peng Wang
A Modulized Three-Port Interlinking Converter for Hybrid AC/DC/DS Microgrids Featured With a Decentralized Power Management Strategy. IEEE Trans. Ind. Electron. 68 (12) (2021)
Chi Jin, Qinghua Liu, Sobhan Miryoosefi
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms. NeurIPS (2021)
Sobhan Miryoosefi, Chi Jin
A Simple Reward-free Approach to Constrained Reinforcement Learning. CoRR (2021)
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. ICML (2021)
Bowen Yi, Chi Jin, Lei Wang, Guodong Shi, Ian R. Manchester
An almost globally convergent observer for visual SLAM without persistent excitation. CDC (2021)
Dipendra Misra, Qinghua Liu, Chi Jin, John Langford
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. ICLR (2021)
Bowen Yi, Chi Jin, Lei Wang, Guodong Shi, Ian R. Manchester
An almost globally convergent observer for visual SLAM without persistent excitation. CoRR (2021)
Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. ICML (2020)
Liang Shao, Chi Jin, Arno Eichberger, Cornelia Lex
Grid Search Based Tire-Road Friction Estimation. IEEE Access 8 (2020)
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS (2020)
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
Reward-Free Exploration for Reinforcement Learning. CoRR (2020)
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
Reward-Free Exploration for Reinforcement Learning. ICML (2020)
Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. CoRR (2020)
Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
Provably efficient reinforcement learning with linear function approximation. COLT (2020)
Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. NeurIPS (2020)
Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. NeurIPS (2020)
Tianyi Lin, Chi Jin, Michael I. Jordan
Near-Optimal Algorithms for Minimax Optimization. CoRR (2020)
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. CoRR (2020)
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML (2020)
Yu Bai, Chi Jin
Provable Self-Play Algorithms for Competitive Reinforcement Learning. ICML (2020)
Yu Bai, Chi Jin
Provable Self-Play Algorithms for Competitive Reinforcement Learning. CoRR (2020)
Yu Bai, Chi Jin, Tiancheng Yu
Near-Optimal Reinforcement Learning with Self-Play. CoRR (2020)
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
On the Theory of Transfer Learning: The Importance of Task Diversity. CoRR (2020)
Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
Provably Efficient Exploration in Policy Optimization. ICML (2020)
Tianyi Lin, Chi Jin, Michael I. Jordan
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML (2020)
Tianyi Lin, Chi Jin, Michael I. Jordan
Near-Optimal Algorithms for Minimax Optimization. COLT (2020)
Pengfeng Lin, Chuanlin Zhang, Jinyu Wang, Chi Jin, Peng Wang
On Autonomous Large-Signal Stabilization for Islanded Multibus DC Microgrids: A Uniform Nonsmooth Control Scheme. IEEE Trans. Ind. Electron. 67 (6) (2020)
Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
Provable Meta-Learning of Linear Representations. CoRR (2020)
Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan
Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. CoRR (2020)
Yu Bai, Chi Jin, Tiancheng Yu
Near-Optimal Reinforcement Learning with Self-Play. NeurIPS (2020)
Pengfeng Lin, Chi Jin, Jianfang Xiao, Xiaoqiang Li, Donghan Shi, Yi Tang, Peng Wang
A Distributed Control Architecture for Global System Economic Operation in Autonomous Hybrid AC/DC Microgrids. IEEE Trans. Smart Grid 10 (3) (2019)
Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
Provably Efficient Exploration in Policy Optimization. CoRR (2019)
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR (2019)
Chi Jin, Keqin Gu, Islam Boussaada, Silviu-Iulian Niculescu
Stability Analysis of a More General Class of Systems With Delay-Dependent Coefficients. IEEE Trans. Autom. Control. 64 (5) (2019)
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR (2019)
Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
Provably Efficient Reinforcement Learning with Linear Function Approximation. CoRR (2019)
Pengfeng Lin, Peng Wang, Chi Jin, Jianfang Xiao, Xiaoqiang Li, Fanghong Guo, Chuanlin Zhang
A Distributed Power Management Strategy for Multi-Paralleled Bidirectional Interlinking Converters in Hybrid AC/DC Microgrids. IEEE Trans. Smart Grid 10 (5) (2019)
Tianyi Lin, Chi Jin, Michael I. Jordan
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. CoRR (2019)
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR (2019)
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR (2018)
Yuansi Chen, Chi Jin, Bin Yu
Stability and Convergence Trade-off of Iterative Optimization Algorithms. CoRR (2018)
Chi Jin, Keqin Gu, Silviu-Iulian Niculescu, Islam Boussaada
Stability Analysis of Systems With Delay-Dependent Coefficients: An Overview. IEEE Access 6 (2018)
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan
Sampling Can Be Faster Than Optimization. CoRR (2018)
Junjun Wang, Chi Jin, Peng Wang
A Uniform Control Strategy for the Interlinking Converter in Hierarchical Controlled Hybrid AC/DC Microgrids. IEEE Trans. Ind. Electron. 65 (8) (2018)
Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan
Is Q-learning Provably Efficient? CoRR (2018)
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. COLT (2018)
Chi Jin, Ruochun Jin, Kai Chen, Yong Dou
A Community Detection Approach to Cleaning Extremely Large Face Database. Comput. Intell. Neurosci. 2018 (2018)
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS (2018)
Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan
Is Q-Learning Provably Efficient? NeurIPS (2018)
Chi Jin, Keqin Gu, Silviu-Iulian Niculescu, Islam Boussaada
Stability Analysis of Systems with Delay-Dependent Coefficients Subject to Some Particular Delay Structure. ECC (2018)
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
On the Local Minima of the Empirical Risk. NeurIPS (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Chi Jin