Chi Jin - RankMe

160 papers A* 59A 1B 1Journal 93Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms. CoRR Haoyu Zhao, Ziran Yang, Jiawei Li, Deyuan He, Zenan Li, Chi Jin, Venugopal V. Veeravalli, Aarti Gupta, Sanjeev Arora
2026	J	`jnl`	Automatic Generation of High-Performance RL Environments. CoRR Seth Karten, Rahul Dev Appapogu, Chi Jin
2026	J	`jnl`	Goedel-Code-Prover: Hierarchical Proof Search for Open State-of-the-Art Code Verification. CoRR Zenan Li, Ziran Yang, Deyuan He, Haoyu Zhao, Andrew Zhao, Shange Tang, Kaiyu Yang, Aarti Gupta, Zhendong Su, Chi Jin
2026	J	`jnl`	Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial? Trans. Mach. Learn. Res. Wenzhe Li, Yong Lin, Mengzhou Xia, Chi Jin
2025	A*	`conf`	Benign Overfitting in Out-of-Distribution Generalization of Linear Models. ICLR Shange Tang, Jiayun Wu, Jianqing Fan, Chi Jin
2025	J	`jnl`	Frontier LLMs Still Struggle with Simple Reasoning Tasks. CoRR Alan Malek, Jiawei Ge, Nevena Lazic, Chi Jin, András György, Csaba Szepesvári
2025	J	`jnl`	Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction. CoRR Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui-Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, Jiayun Wu, Jiri Gesi, Ximing Lu, David Acuna, Kaiyu Yang, Hongzhou Lin, Yejin Choi, Danqi Chen, Sanjeev Arora, Chi Jin
2025	J	`jnl`	Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving. CoRR Yong Lin, Shange Tang, Bohan Lyu, Jiayun Wu, Hongzhou Lin, Kaiyu Yang, Jia Li, Mengzhou Xia, Danqi Chen, Sanjeev Arora, Chi Jin
2025	J	`jnl`	Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving on Inequalities. CoRR Haoyu Zhao, Yihan Geng, Shange Tang, Yong Lin, Bohan Lyu, Hongzhou Lin, Chi Jin, Sanjeev Arora
2025	J	`jnl`	Is Elo Rating Reliable? A Study Under Model Misspecification. CoRR Shange Tang, Yuanhao Wang, Chi Jin
2025	J	`jnl`	LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra. CoRR Seth Karten, Wenzhe Li, Zihan Ding, Samuel Kleiner, Yu Bai, Chi Jin
2025	J	`jnl`	Learning World Models for Interactive Video Generation. CoRR Taiye Chen, Xun Hu, Zihan Ding, Chi Jin
2025	A*	`conf`	MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. ICML 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
2025	J	`jnl`	MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. CoRR 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
2025	A*	`conf`	PokéChamp: an Expert-level Minimax Language Agent. ICML Seth Karten, Andy Luu Nguyen, Chi Jin
2025	J	`jnl`	PokéChamp: an Expert-level Minimax Language Agent. CoRR Seth Karten, Andy Luu Nguyen, Chi Jin
2025	J	`jnl`	Principled Out-of-Distribution Generalization via Simplicity. CoRR Jiawei Ge, Amanda Wang, Shange Tang, Chi Jin
2025	A*	`conf`	Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games. ICML Jiawei Ge, Yuanhao Wang, Wenzhe Li, Chi Jin
2025	J	`jnl`	Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. J. Mach. Learn. Res. Tianyi Lin, Chi Jin, Michael I. Jordan
2025	J	`jnl`	Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration. CoRR Ahmed Khaled, Satyen Kale, Arthur Douillard, Chi Jin, Rob Fergus, Manzil Zaheer
2024	J	`jnl`	Benign Overfitting in Out-of-Distribution Generalization of Linear Models. CoRR Shange Tang, Jiayun Wu, Jianqing Fan, Chi Jin
2024	A*	`conf`	Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning. ICLR Zihan Ding, Chi Jin
2024	A*	`conf`	FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning. ICML Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin
2024	J	`jnl`	FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning. CoRR Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin
2024	J	`jnl`	Generative Diffusion Modeling: A Practical Handbook. CoRR Zihan Ding, Chi Jin
2024	A*	`conf`	Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift. ICLR Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin
2024	J	`jnl`	On Limitation of Transformer for Learning HMMs. CoRR Jiachen Hu, Qinghua Liu, Chi Jin
2024	A*	`conf`	On the Provable Advantage of Unsupervised Pretraining. ICLR Jiawei Ge, Shange Tang, Jianqing Fan, Chi Jin
2024	B	`conf`	Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol. AFT Mahsa Bastankhah, Viraj Nadkarni, Chi Jin, Sanjeev Kulkarni, Pramod Viswanath
2024	J	`jnl`	Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol. CoRR Mahsa Bastankhah, Viraj Nadkarni, Xuechao Wang, Chi Jin, Sanjeev Kulkarni, Pramod Viswanath
2024	J	`jnl`	Towards Principled Superhuman AI for Multiplayer Symmetric Games. CoRR Jiawei Ge, Yuanhao Wang, Wenzhe Li, Chi Jin
2024	A*	`conf`	Tuning-Free Stochastic Optimization. ICML Ahmed Khaled, Chi Jin
2024	J	`jnl`	Tuning-Free Stochastic Optimization. CoRR Ahmed Khaled, Chi Jin
2024	J	`jnl`	Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization. CoRR Tianyi Lin, Chi Jin, Michael I. Jordan
2024	J	`jnl`	V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent Reinforcement Learning. Math. Oper. Res. Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu
2024	—	`conf`	ZeroSwap: Data-Driven Optimal Market Making in Decentralized Finance. FC (1) Viraj Nadkarni, Jiachen Hu, Ranvir Rana, Chi Jin, Sanjeev Kulkarni, Pramod Viswanath
2023	A*	`conf`	Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. COLT Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin
2023	J	`jnl`	Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. CoRR Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin
2023	J	`jnl`	Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning. CoRR Zihan Ding, Chi Jin
2023	A*	`conf`	Context-lumpable stochastic bandits. NeurIPS Chung-wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
2023	J	`jnl`	Context-lumpable stochastic bandits. CoRR Chung-wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
2023	A*	`conf`	DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. NeurIPS Ahmed Khaled, Konstantin Mishchenko, Chi Jin
2023	J	`jnl`	DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. CoRR Ahmed Khaled, Konstantin Mishchenko, Chi Jin
2023	A*	`conf`	Efficient displacement convex optimization with particle gradient descent. ICML Hadi Daneshmand, Jason D. Lee, Chi Jin
2023	J	`jnl`	Efficient displacement convex optimization with particle gradient descent. CoRR Hadi Daneshmand, Jason D. Lee, Chi Jin
2023	A*	`conf`	Faster federated optimization under second-order similarity. ICLR Ahmed Khaled, Chi Jin
2023	J	`jnl`	Is RLHF More Difficult than Standard RL? CoRR Yuanhao Wang, Qinghua Liu, Chi Jin
2023	A*	`conf`	Is RLHF More Difficult than Standard RL? A Theoretical Perspective. NeurIPS Yuanhao Wang, Qinghua Liu, Chi Jin
2023	A*	`conf`	Learning Rationalizable Equilibria in Multiplayer Games. ICLR Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin
2023	J	`jnl`	Learning a Universal Human Prior for Dexterous Manipulation from Human Preference. CoRR Zihan Ding, Yuanpei Chen, Allen Z. Ren, Shixiang Shane Gu, Hao Dong, Chi Jin
2023	J	`jnl`	Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift. CoRR Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin
2023	J	`jnl`	On the Provable Advantage of Unsupervised Pretraining. CoRR Jiawei Ge, Shange Tang, Jianqing Fan, Chi Jin
2023	A*	`conf`	Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. STOC Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
2023	A*	`conf`	Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. NeurIPS Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
2023	J	`jnl`	Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. CoRR Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
2023	A*	`conf`	Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. ICLR Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang
2023	J	`jnl`	Provably Efficient Reinforcement Learning with Linear Function Approximation. Math. Oper. Res. Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2023	A*	`conf`	Representation Learning for Low-rank General-sum Markov Games. ICLR Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang
2023	J	`jnl`	ZeroSwap: Data-driven Optimal Market Making in DeFi. CoRR Viraj Nadkarni, Jiachen Hu, Ranvir Rana, Chi Jin, Sanjeev R. Kulkarni, Pramod Viswanath
2022	J	`jnl`	A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games. CoRR Zihan Ding, DiJia Su, Qinghua Liu, Chi Jin
2022	A*	`conf`	A Simple Reward-free Approach to Constrained Reinforcement Learning. ICML Sobhan Miryoosefi, Chi Jin
2022	A*	`conf`	Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent. NeurIPS Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu
2022	J	`jnl`	Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. CoRR Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu
2022	J	`jnl`	Faster federated optimization under second-order similarity. CoRR Ahmed Khaled, Chi Jin
2022	A*	`conf`	Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. ICML Qinghua Liu, Yuanhao Wang, Chi Jin
2022	J	`jnl`	Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. CoRR Qinghua Liu, Yuanhao Wang, Chi Jin
2022	J	`jnl`	Learning Rationalizable Equilibria in Multiplayer Games. CoRR Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin
2022	A*	`conf`	Minimax Optimization with Smooth Algorithmic Adversaries. ICLR Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
2022	A*	`conf`	Near-Optimal Learning of Extensive-Form Games with Imperfect Information. ICML Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
2022	J	`jnl`	Near-Optimal Learning of Extensive-Form Games with Imperfect Information. CoRR Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
2022	J	`jnl`	Optimistic MLE - A Generic Model-based Algorithm for Partially Observable Sequential Decision Making. CoRR Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
2022	A*	`conf`	Provable Reinforcement Learning with a Short-Term Memory. ICML Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi
2022	J	`jnl`	Provable Reinforcement Learning with a Short-Term Memory. CoRR Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi
2022	J	`jnl`	Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. CoRR Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang
2022	J	`jnl`	Representation Learning for General-sum Low-rank Markov Games. CoRR Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, Mengdi Wang
2022	A*	`conf`	Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. NeurIPS Qinghua Liu, Csaba Szepesvári, Chi Jin
2022	J	`jnl`	Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. CoRR Qinghua Liu, Csaba Szepesvári, Chi Jin
2022	A*	`conf`	The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. ICML Chi Jin, Qinghua Liu, Tiancheng Yu
2022	A*	`conf`	Understanding Domain Randomization for Sim-to-real Transfer. ICLR Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang
2022	A*	`conf`	When Is Partially Observable Reinforcement Learning Not Scary? COLT Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
2022	J	`jnl`	When Is Partially Observable Reinforcement Learning Not Scary? CoRR Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
2021	A*	`conf`	A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. COLT Mo Zhou, Rong Ge, Chi Jin
2021	J	`jnl`	A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. CoRR Mo Zhou, Rong Ge, Chi Jin
2021	A*	`conf`	A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. ICML Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
2021	J	`jnl`	A Simple Reward-free Approach to Constrained Reinforcement Learning. CoRR Sobhan Miryoosefi, Chi Jin
2021	A*	`conf`	Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms. NeurIPS Chi Jin, Qinghua Liu, Sobhan Miryoosefi
2021	J	`jnl`	Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms. CoRR Chi Jin, Qinghua Liu, Sobhan Miryoosefi
2021	J	`jnl`	Minimax Optimization with Smooth Algorithmic Adversaries. CoRR Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
2021	A*	`conf`	Near-Optimal Representation Learning for Linear Bandits and Linear RL. ICML Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang
2021	J	`jnl`	Near-optimal Representation Learning for Linear Bandits and Linear RL. CoRR Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang
2021	J	`jnl`	On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2021	A*	`conf`	Provable Meta-Learning of Linear Representations. ICML Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
2021	A*	`conf`	Provable Rich Observation Reinforcement Learning with Combinatorial Latent States. ICLR Dipendra Misra, Qinghua Liu, Chi Jin, John Langford
2021	A*	`conf`	Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. NeurIPS Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
2021	J	`jnl`	Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games. CoRR Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
2021	J	`jnl`	The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. CoRR Chi Jin, Qinghua Liu, Tiancheng Yu
2021	J	`jnl`	Understanding Domain Randomization for Sim-to-real Transfer. CoRR Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang
2021	J	`jnl`	V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent RL. CoRR Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu
2020	J	`jnl`	A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. CoRR Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
2020	J	`jnl`	Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. CoRR Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan
2020	A*	`conf`	Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. ICML Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu
2020	A*	`conf`	Near-Optimal Algorithms for Minimax Optimization. COLT Tianyi Lin, Chi Jin, Michael I. Jordan
2020	J	`jnl`	Near-Optimal Algorithms for Minimax Optimization. CoRR Tianyi Lin, Chi Jin, Michael I. Jordan
2020	A*	`conf`	Near-Optimal Reinforcement Learning with Self-Play. NeurIPS Yu Bai, Chi Jin, Tiancheng Yu
2020	J	`jnl`	Near-Optimal Reinforcement Learning with Self-Play. CoRR Yu Bai, Chi Jin, Tiancheng Yu
2020	A*	`conf`	On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. ICML Tianyi Lin, Chi Jin, Michael I. Jordan
2020	A*	`conf`	On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
2020	J	`jnl`	On the Theory of Transfer Learning: The Importance of Task Diversity. CoRR Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
2020	J	`jnl`	Provable Meta-Learning of Linear Representations. CoRR Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
2020	A*	`conf`	Provable Self-Play Algorithms for Competitive Reinforcement Learning. ICML Yu Bai, Chi Jin
2020	J	`jnl`	Provable Self-Play Algorithms for Competitive Reinforcement Learning. CoRR Yu Bai, Chi Jin
2020	A*	`conf`	Provably Efficient Exploration in Policy Optimization. ICML Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
2020	A*	`conf`	Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. NeurIPS Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan
2020	A*	`conf`	Provably efficient reinforcement learning with linear function approximation. COLT Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2020	A*	`conf`	Reward-Free Exploration for Reinforcement Learning. ICML Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
2020	J	`jnl`	Reward-Free Exploration for Reinforcement Learning. CoRR Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu
2020	A*	`conf`	Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. NeurIPS Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu
2020	J	`jnl`	Sample-Efficient Reinforcement Learning of Undercomplete POMDPs. CoRR Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu
2020	A*	`conf`	What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2019	J	`jnl`	A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2019	—	`—`	Machine Learning: Why Do Simple Algorithms Work So Well? Chi Jin
2019	J	`jnl`	Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2019	J	`jnl`	On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems. CoRR Tianyi Lin, Chi Jin, Michael I. Jordan
2019	J	`jnl`	Provably Efficient Exploration in Policy Optimization. CoRR Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
2019	J	`jnl`	Provably Efficient Reinforcement Learning with Linear Function Approximation. CoRR Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
2019	J	`jnl`	Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2018	A*	`conf`	Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. COLT Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2018	A*	`conf`	Is Q-Learning Provably Efficient? NeurIPS Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan
2018	J	`jnl`	Is Q-learning Provably Efficient? CoRR Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan
2018	J	`jnl`	Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
2018	A*	`conf`	On the Local Minima of the Empirical Risk. NeurIPS Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
2018	J	`jnl`	Sampling Can Be Faster Than Optimization. CoRR Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan
2018	J	`jnl`	Stability and Convergence Trade-off of Iterative Optimization Algorithms. CoRR Yuansi Chen, Chi Jin, Bin Yu
2018	A*	`conf`	Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
2017	J	`jnl`	Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. CoRR Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2017	A	`conf`	Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. AISTATS Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2017	—	`conf`	Gradient Descent Can Take Exponential Time to Escape Saddle Points. NIPS Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos
2017	J	`jnl`	Gradient Descent Can Take Exponential Time to Escape Saddle Points. CoRR Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabás Póczos, Aarti Singh
2017	A*	`conf`	How to Escape Saddle Points Efficiently. ICML Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	J	`jnl`	How to Escape Saddle Points Efficiently. CoRR Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	A*	`conf`	No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. ICML Rong Ge, Chi Jin, Yi Zheng
2017	J	`jnl`	No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. CoRR Rong Ge, Chi Jin, Yi Zheng
2017	J	`jnl`	Stochastic Cubic Regularization for Fast Nonconvex Optimization. CoRR Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
2016	J	`jnl`	Differentially Private Data Releasing for Smooth Queries. J. Mach. Learn. Res. Ziteng Wang, Chi Jin, Kai Fan, Jiaqi Zhang, Junliang Huang, Yiqiao Zhong, Liwei Wang
2016	A*	`conf`	Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. ICML Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	J	`jnl`	Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. CoRR Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	A*	`conf`	Faster Eigenvector Computation via Shift-and-Invert Preconditioning. ICML Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
2016	J	`jnl`	Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
2016	—	`conf`	Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. NIPS Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
2016	J	`jnl`	Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. CoRR Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
2016	J	`jnl`	Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. CoRR Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	—	`conf`	Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. NIPS Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2016	J	`jnl`	Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. CoRR Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2016	A*	`conf`	Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. COLT Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2015	J	`jnl`	Computing Matrix Squareroot via Non Convex Local Search. CoRR Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2015	A*	`conf`	Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. COLT Rong Ge, Furong Huang, Chi Jin, Yang Yuan
2015	J	`jnl`	Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. CoRR Rong Ge, Furong Huang, Chi Jin, Yang Yuan
2015	J	`jnl`	Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. CoRR Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
2014	J	`jnl`	Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output. CoRR Chi Jin, Ziteng Wang, Junliang Huang, Yiqiao Zhong, Liwei Wang
2012	—	`conf`	Dimensionality Dependent PAC-Bayes Margin Bound. NIPS Chi Jin, Liwei Wang