Chi Jin

Chi Jin

Princeton University

H-index: 40

North America-United States

About Chi Jin

Chi Jin, With an exceptional h-index of 40 and a recent h-index of 38 (since 2020), a distinguished researcher at Princeton University, specializes in the field of Machine Learning, Optimization.

His recent articles reflect a diverse array of research interests and contributions to the field:

Optimistic natural policy gradient: a simple efficient policy optimization framework for online rl

Is RLHF More Difficult than Standard RL? A Theoretical Perspective

Context-lumpable stochastic bandits

Learning a universal human prior for dexterous manipulation from human preference

On the provable advantage of unsupervised pretraining

Maximum likelihood estimation is all you need for well-specified covariate shift

V-learning—a simple, efficient, decentralized algorithm for multiagent reinforcement learning

ZeroSwap: Data-driven Optimal Market Making in DeFi

Chi Jin Information

University

Position

Assistant Professor

Citations(all)

9455

Citations(since 2020)

8481

Cited By

3617

hIndex(all)

40

hIndex(since 2020)

38

i10Index(all)

54

i10Index(since 2020)

53

Email

University Profile Page

Google Scholar

Chi Jin Skills & Research Interests

Machine Learning

Optimization

Top articles of Chi Jin

Optimistic natural policy gradient: a simple efficient policy optimization framework for online rl

Thirty-seventh Conference on Neural Information Processing Systems

2023/5/18

Qinghua Liu
Qinghua Liu

H-Index: 34

Chi Jin
Chi Jin

H-Index: 24

Is RLHF More Difficult than Standard RL? A Theoretical Perspective

Advances in Neural Information Processing Systems

2024/2/13

Context-lumpable stochastic bandits

Advances in Neural Information Processing Systems

2024/2/13

Learning a universal human prior for dexterous manipulation from human preference

arXiv preprint arXiv:2304.04602

2023/4/10

On the provable advantage of unsupervised pretraining

arXiv preprint arXiv:2303.01566

2023/3/2

Jiawei Ge
Jiawei Ge

H-Index: 1

Chi Jin
Chi Jin

H-Index: 24

Maximum likelihood estimation is all you need for well-specified covariate shift

arXiv preprint arXiv:2311.15961

2023/11/27

V-learning—a simple, efficient, decentralized algorithm for multiagent reinforcement learning

Mathematics of Operations Research

2023/11/17

ZeroSwap: Data-driven Optimal Market Making in DeFi

arXiv preprint arXiv:2310.09413

2023/10/13

Consistency models as a rich and efficient policy class for reinforcement learning

2023/9/29

Zihan Ding
Zihan Ding

H-Index: 6

Chi Jin
Chi Jin

H-Index: 24

Provably Efficient Reinforcement Learning with Linear Function Approximation

Mathematics of Operations Research/Annual Conference on Learning Theory

2022

Breaking the curse of multiagency: Provably efficient decentralized multi-agent rl with function approximation

Conference on Learning Theory, 2023

2023/2/13

Is RLHF More Difficult than Standard RL?

arXiv preprint arXiv:2306.14111

2023/6/25

Optimistic mle: A generic model-based algorithm for partially observable sequential decision making

2023/6/2

The power of exploiter: Provable multi-agent rl in large state spaces

2022/6/28

Sample-efficient reinforcement learning of partially observable markov games

Advances in Neural Information Processing Systems

2022/12/6

Qinghua Liu
Qinghua Liu

H-Index: 34

Chi Jin
Chi Jin

H-Index: 24

Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent

Advances in Neural Information Processing Systems

2022/12/6

Representation learning for general-sum low-rank markov games

arXiv preprint arXiv:2210.16976

2022/10/30

Provable sim-to-real transfer in continuous domain with partial observations

arXiv preprint arXiv:2210.15598

2022/10/27

Learning Rationalizable Equilibria in Multiplayer Games

arXiv preprint arXiv:2210.11402

2022/10/20

Representation learning for low-rank general-sum markov games

2022/9/29

See List of Professors in Chi Jin University(Princeton University)

Co-Authors

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