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We stand with Ukraine

Chi Jin

Orcid: 0000-0002-2865-5610

Affiliations:

Princeton University, Department of Electrical Engineering, NJ, USA
University of California, Berkeley, USA (PhD 2019)

According to our database¹, Chi Jin authored at least 86 papers between 2012 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

Online presence:

on orcid.org

On csauthors.net:

Bibliography

2024

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization.

[BibT_eX]

[DOI]

,

,

Michael I. Jordan

CoRR, 2024

Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol.

[BibT_eX]

[DOI]

Mahsa Bastankhah

,

,

,

,

Sanjeev Kulkarni

,

Pramod Viswanath

CoRR, 2024

Towards Principled Superhuman AI for Multiplayer Symmetric Games.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2024

On Limitation of Transformer for Learning HMMs.

[BibT_eX]

[DOI]

,

,

CoRR, 2024

FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Tuning-Free Stochastic Optimization.

[BibT_eX]

[DOI]

,

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift.

[BibT_eX]

[DOI]

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,

,

,

Proceedings of the Twelfth International Conference on Learning Representations, 2024

On the Provable Advantage of Unsupervised Pretraining.

[BibT_eX]

[DOI]

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,

,

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning.

[BibT_eX]

[DOI]

,

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol.

[BibT_eX]

[DOI]

Mahsa Bastankhah

,

,

,

Sanjeev Kulkarni

,

Pramod Viswanath

Proceedings of the 6th Conference on Advances in Financial Technologies, 2024

2023

Provably Efficient Reinforcement Learning with Linear Function Approximation.

[BibT_eX]

[DOI]

,

,

,

Michael I. Jordan

Math. Oper. Res., August, 2023

ZeroSwap: Data-driven Optimal Market Making in DeFi.

[BibT_eX]

[DOI]

,

,

,

,

Sanjeev R. Kulkarni

,

Pramod Viswanath

CoRR, 2023

Is RLHF More Difficult than Standard RL?

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Learning a Universal Human Prior for Dexterous Manipulation from Human Preference.

[BibT_eX]

[DOI]

,

,

,

Shixiang Shane Gu

,

,

CoRR, 2023

Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making.

[BibT_eX]

[DOI]

,

Praneeth Netrapalli

,

Csaba Szepesvári

,

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL.

[BibT_eX]

[DOI]

,

,

András György

,

,

Csaba Szepesvári

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Context-lumpable stochastic bandits.

[BibT_eX]

[DOI]

,

,

Yasin Abbasi-Yadkori

,

,

,

Csaba Szepesvári

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method.

[BibT_eX]

[DOI]

,

Konstantin Mishchenko

,

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Is RLHF More Difficult than Standard RL? A Theoretical Perspective.

[BibT_eX]

[DOI]

,

,

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient displacement convex optimization with particle gradient descent.

[BibT_eX]

[DOI]

Hadi Daneshmand

,

,

Proceedings of the International Conference on Machine Learning, 2023

Representation Learning for Low-rank General-sum Markov Games.

[BibT_eX]

[DOI]

,

,

,

,

,

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Provable Sim-to-real Transfer in Continuous Domain with Partial Observations.

[BibT_eX]

[DOI]

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,

,

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning Rationalizable Equilibria in Multiplayer Games.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Faster federated optimization under second-order similarity.

[BibT_eX]

[DOI]

,

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Representation Learning for General-sum Low-rank Markov Games.

[BibT_eX]

[DOI]

,

,

,

,

CoRR, 2022

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2022

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

[BibT_eX]

[DOI]

,

,

,

,

CoRR, 2022

Sample-Efficient Reinforcement Learning of Partially Observable Markov Games.

[BibT_eX]

[DOI]

,

Csaba Szepesvári

,

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

[BibT_eX]

[DOI]

,

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Simple Reward-free Approach to Constrained Reinforcement Learning.

[BibT_eX]

[DOI]

Sobhan Miryoosefi

,

Proceedings of the International Conference on Machine Learning, 2022

Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits.

[BibT_eX]

[DOI]

,

,

Proceedings of the International Conference on Machine Learning, 2022

The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces.

[BibT_eX]

[DOI]

,

,

Proceedings of the International Conference on Machine Learning, 2022

Provable Reinforcement Learning with a Short-Term Memory.

[BibT_eX]

[DOI]

Yonathan Efroni

,

,

Akshay Krishnamurthy

,

Sobhan Miryoosefi

Proceedings of the International Conference on Machine Learning, 2022

Near-Optimal Learning of Extensive-Form Games with Imperfect Information.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the International Conference on Machine Learning, 2022

Minimax Optimization with Smooth Algorithmic Adversaries.

[BibT_eX]

[DOI]

,

,

Praneeth Netrapalli

,

Lillian J. Ratliff

Proceedings of the Tenth International Conference on Learning Representations, 2022

Understanding Domain Randomization for Sim-to-real Transfer.

[BibT_eX]

[DOI]

,

,

,

,

Proceedings of the Tenth International Conference on Learning Representations, 2022

When Is Partially Observable Reinforcement Learning Not Scary?

[BibT_eX]

[DOI]

,

,

Csaba Szepesvári

,

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points.

[BibT_eX]

[DOI]

,

Praneeth Netrapalli

,

,

,

Michael I. Jordan

J. ACM, 2021

V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent RL.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2021

Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms.

[BibT_eX]

[DOI]

,

,

Sobhan Miryoosefi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Provable Meta-Learning of Linear Representations.

[BibT_eX]

[DOI]

Nilesh Tripuraneni

,

,

Michael I. Jordan

Proceedings of the 38th International Conference on Machine Learning, 2021

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the 38th International Conference on Machine Learning, 2021

Near-Optimal Representation Learning for Linear Bandits and Linear RL.

[BibT_eX]

[DOI]

,

,

,

,

Proceedings of the 38th International Conference on Machine Learning, 2021

Provable Rich Observation Reinforcement Learning with Combinatorial Latent States.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the 9th International Conference on Learning Representations, 2021

A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network.

[BibT_eX]

[DOI]

,

,

Proceedings of the Conference on Learning Theory, 2021

2020

Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations.

[BibT_eX]

[DOI]

,

,

,

,

Michael I. Jordan

CoRR, 2020

Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations.

[BibT_eX]

[DOI]

,

,

,

,

Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the Theory of Transfer Learning: The Importance of Task Diversity.

[BibT_eX]

[DOI]

Nilesh Tripuraneni

,

Michael I. Jordan

,

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs.

[BibT_eX]

[DOI]

,

,

Akshay Krishnamurthy

,

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Near-Optimal Reinforcement Learning with Self-Play.

[BibT_eX]

[DOI]

,

,

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems.

[BibT_eX]

[DOI]

,

,

Michael I. Jordan

Proceedings of the 37th International Conference on Machine Learning, 2020

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

[BibT_eX]

[DOI]

,

Praneeth Netrapalli

,

Michael I. Jordan

Proceedings of the 37th International Conference on Machine Learning, 2020

Reward-Free Exploration for Reinforcement Learning.

[BibT_eX]

[DOI]

,

Akshay Krishnamurthy

,

,

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition.

[BibT_eX]

[DOI]

,

,

,

,

Proceedings of the 37th International Conference on Machine Learning, 2020

Provably Efficient Exploration in Policy Optimization.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the 37th International Conference on Machine Learning, 2020

Provable Self-Play Algorithms for Competitive Reinforcement Learning.

[BibT_eX]

[DOI]

,

Proceedings of the 37th International Conference on Machine Learning, 2020

Near-Optimal Algorithms for Minimax Optimization.

[BibT_eX]

[DOI]

,

,

Michael I. Jordan

Proceedings of the Conference on Learning Theory, 2020

2019

Machine Learning: Why Do Simple Algorithms Work So Well?

[BibT_eX]

[DOI]

PhD thesis, 2019

Stochastic Gradient Descent Escapes Saddle Points Efficiently.

[BibT_eX]

[DOI]

,

Praneeth Netrapalli

,

,

,

Michael I. Jordan

CoRR, 2019

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm.

[BibT_eX]

[DOI]

,

Praneeth Netrapalli

,

,

,

Michael I. Jordan

CoRR, 2019

Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal.

[BibT_eX]

[DOI]

,

Praneeth Netrapalli

,

Michael I. Jordan

CoRR, 2019

2018

Sampling Can Be Faster Than Optimization.

[BibT_eX]

[DOI]

,

,

,

Nicolas Flammarion

,

Michael I. Jordan

CoRR, 2018

Stability and Convergence Trade-off of Iterative Optimization Algorithms.

[BibT_eX]

[DOI]

,

,

CoRR, 2018

Minimizing Nonconvex Population Risk from Rough Empirical Risk.

[BibT_eX]

[DOI]

,

,

,

Michael I. Jordan

CoRR, 2018

Stochastic Cubic Regularization for Fast Nonconvex Optimization.

[BibT_eX]

[DOI]

Nilesh Tripuraneni

,

,

,

,

Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On the Local Minima of the Empirical Risk.

[BibT_eX]

[DOI]

,

,

,

Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Is Q-Learning Provably Efficient?

[BibT_eX]

[DOI]

,

Zeyuan Allen-Zhu

,

Sébastien Bubeck

,

Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent.

[BibT_eX]

[DOI]

,

Praneeth Netrapalli

,

Michael I. Jordan

Proceedings of the Conference On Learning Theory, 2018

2017

Gradient Descent Can Take Exponential Time to Escape Saddle Points.

[BibT_eX]

[DOI]

,

,

,

Michael I. Jordan

,

,

Barnabás Póczos

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

How to Escape Saddle Points Efficiently.

[BibT_eX]

[DOI]

,

,

Praneeth Netrapalli

,

,

Michael I. Jordan

Proceedings of the 34th International Conference on Machine Learning, 2017

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis.

[BibT_eX]

[DOI]

,

,

Proceedings of the 34th International Conference on Machine Learning, 2017

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot.

[BibT_eX]

[DOI]

,

,

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Praneeth Netrapalli

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Differentially Private Data Releasing for Smooth Queries.

[BibT_eX]

[DOI]

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,

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,

,

,

J. Mach. Learn. Res., 2016

Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm.

[BibT_eX]

[DOI]

,

,

,

Praneeth Netrapalli

,

CoRR, 2016

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences.

[BibT_eX]

[DOI]

,

,

Sivaraman Balakrishnan

,

Martin J. Wainwright

,

Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent.

[BibT_eX]

[DOI]

,

,

Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis.

[BibT_eX]

[DOI]

,

,

,

Praneeth Netrapalli

,

Proceedings of the 33nd International Conference on Machine Learning, 2016

Faster Eigenvector Computation via Shift-and-Invert Preconditioning.

[BibT_eX]

[DOI]

,

,

,

,

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Praneeth Netrapalli

,

Proceedings of the 33nd International Conference on Machine Learning, 2016

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm.

[BibT_eX]

[DOI]

,

,

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Praneeth Netrapalli

,

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation.

[BibT_eX]

[DOI]

,

,

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Praneeth Netrapalli

,

CoRR, 2015

Computing Matrix Squareroot via Non Convex Local Search.

[BibT_eX]

[DOI]

,

,

,

Praneeth Netrapalli

CoRR, 2015

Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition.

[BibT_eX]

[DOI]

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,

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Proceedings of The 28th Conference on Learning Theory, 2015

2014

Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output.

[BibT_eX]

[DOI]

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CoRR, 2014

2012

Dimensionality Dependent PAC-Bayes Margin Bound.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

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