229 citations · 731 across the 35 of their papers we have counts for
6 papers · 2 filters
Provably Efficient Exploration in Policy Optimization
Qi Cai, Zhuoran Yang, Chi Jin +1
While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL. In…
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
Chi Jin, Tiancheng Jin, Haipeng Luo +2
We consider the problem of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses. We propose an…
Provably Efficient Reinforcement Learning with Linear Function Approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang +1
Modern Reinforcement Learning (RL) is commonly applied to practical problems with an enormous number of states, where function approximation must be deployed to approximate either…
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
Tianyi Lin, Chi Jin, Michael I. Jordan
We consider nonconvex-concave minimax problems, , where is nonconvex in but concave…
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Chi Jin, Praneeth Netrapalli, Rong Ge +2
Gradient descent (GD) and stochastic gradient descent (SGD) are the workhorses of large-scale machine learning. While classical theory focused on analyzing the performance of these…
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent r…