← Authors

Chi Jin

68 papers · 2012–2025 · 7 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) 🐝 Cross-Pollinator (9) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (13) πŸ—ΊοΈ Taxonomy Completionist (19) 🌈 Renaissance Researcher (8) 🏠 Conference Loyalist (25) 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ† Keyword Champion (3) πŸ”¬ Deep Specialist (18) πŸ—ƒοΈ Keyword Collector (65) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (6) ⚑ Prolific Year (10) ❓ The Questioner (4) πŸ’Ž Century Club (68)

Conferences

ICML (25) NIPS (19) ICLR (12) COLT (8) JMLR (2) AISTATS (1) ICCV (1)

Top co-authors

Qinghua Liu (12) Michael I. Jordan (11) Praneeth Netrapalli (9) Yu Bai (8) Tiancheng Yu (7) Rong Ge (6) Michael I Jordan (6) Yuanhao Wang (5) Liwei Wang (5) Zihan Ding (4)

Research topics

Differential Privacy (1) Statistics (1)

Keywords

sample complexity (14) regret bound (9) nash equilibrium (8) non-convex optimization (7) markov game (6) game theory (5) saddle point (5) gradient descent (5) minimax optimization (4) multi-agent reinforcement learning (3) learning theory (3) sample-efficient learning (3) representation learning (3) nonconvex optimization (3) stochastic gradient descent (3) reinforcement learning (3) gradient descent ascent (3) linear function approximation (3) value iteration (2) adversarial learning (2)

Papers

DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization ICCV 2025
Building Math Agents with Multi-Turn Iterative Preference Learning ICLR 2025
Benign Overfitting in Out-of-Distribution Generalization of Linear Models ICLR 2025
MATH-Perturb: Benchmarking LLMs’ Math Reasoning Abilities against Hard Perturbations ICML 2025
Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games ICML 2025
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization JMLR 2025
PokΓ©Champ: an Expert-level Minimax Language Agent ICML 2025
Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning ICLR 2024
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning ICML 2024
Tuning-Free Stochastic Optimization ICML 2024
On the Provable Advantage of Unsupervised Pretraining ICLR 2024
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift ICLR 2024
Faster federated optimization under second-order similarity ICLR 2023
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation COLT 2023
Efficient displacement convex optimization with particle gradient descent ICML 2023
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL NIPS 2023
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method NIPS 2023
Is RLHF More Difficult than Standard RL? A Theoretical Perspective NIPS 2023
Context-lumpable stochastic bandits NIPS 2023
Representation Learning for Low-rank General-sum Markov Games ICLR 2023
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations ICLR 2023
Learning Rationalizable Equilibria in Multiplayer Games ICLR 2023
A Simple Reward-free Approach to Constrained Reinforcement Learning ICML 2022
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games NIPS 2022
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent NIPS 2022
When Is Partially Observable Reinforcement Learning Not Scary? COLT 2022
Minimax Optimization with Smooth Algorithmic Adversaries ICLR 2022
Understanding Domain Randomization for Sim-to-real Transfer ICLR 2022
Near-Optimal Learning of Extensive-Form Games with Imperfect Information ICML 2022
Provable Reinforcement Learning with a Short-Term Memory ICML 2022
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces ICML 2022
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits ICML 2022
Near-Optimal Representation Learning for Linear Bandits and Linear RL ICML 2021
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States ICLR 2021
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games NIPS 2021
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms NIPS 2021
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network COLT 2021
Provable Meta-Learning of Linear Representations ICML 2021
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play ICML 2021
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning ICML 2021
On the Theory of Transfer Learning: The Importance of Task Diversity NIPS 2020
Reward-Free Exploration for Reinforcement Learning ICML 2020
Near-Optimal Reinforcement Learning with Self-Play NIPS 2020
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML 2020
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems ICML 2020
Provable Self-Play Algorithms for Competitive Reinforcement Learning ICML 2020
Provably efficient reinforcement learning with linear function approximation COLT 2020
Near-Optimal Algorithms for Minimax Optimization COLT 2020
Provably Efficient Exploration in Policy Optimization ICML 2020
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition ICML 2020
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs NIPS 2020
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations NIPS 2020
Is Q-Learning Provably Efficient? NIPS 2018
Stochastic Cubic Regularization for Fast Nonconvex Optimization NIPS 2018
On the Local Minima of the Empirical Risk NIPS 2018
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent COLT 2018
Gradient Descent Can Take Exponential Time to Escape Saddle Points NIPS 2017
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot AISTATS 2017
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis ICML 2017
How to Escape Saddle Points Efficiently ICML 2017
Faster Eigenvector Computation via Shift-and-Invert Preconditioning ICML 2016
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences NIPS 2016
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja’s Algorithm COLT 2016
Differentially Private Data Releasing for Smooth Queries JMLR 2016
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent NIPS 2016
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis ICML 2016
Escaping From Saddle Points β€” Online Stochastic Gradient for Tensor Decomposition COLT 2015
Dimensionality Dependent PAC-Bayes Margin Bound NIPS 2012