Faculty Directory

Zhaoran Wang

Assistant Professor of Industrial Engineering and Management Sciences and (by courtesy) Computer Science

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Departments

Industrial Engineering and Management Sciences

Research Interests

The long-term goal of my research is to develop a new generation of data-driven decision-making methods, theory, and systems, which tailor artificial intelligence towards addressing pressing societal challenges. To this end, my research aims at:

(a) making deep reinforcement learning more efficient, both computationally and statistically, in a principled manner to enable its applications in critical domains;

(b) scaling deep reinforcement learning to design and optimize societal-scale multi-agent systems, especially those involving cooperation and/or competition among humans and/or robots.

With this aim in mind, my research interests span across machine learning, optimization, statistics, game theory, and information theory.

Selected Publications

Embed to Control Partially Observed Systems: Representation Learning with

Provable Sample Efficiency

Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

International Conference on Learning Representations (ICLR), 2023

Reinforcement Learning from Partial Observation: Linear Function Approximation with

Provable Sample Efficiency

Qi Cai, Zhuoran Yang, Zhaoran Wang

International Conference on Machine Learning (ICML), 2022

A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and

Application to Actor-Critic

Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang (alphabetical)

SIAM Journal on Optimization (SIOPT), 2022

Is Pessimism Provably Efficient for Offline RL?

Ying Jin, Zhuoran Yang, Zhaoran Wang

International Conference on Machine Learning (ICML), 2021

Principled Exploration via Optimistic Bootstrapping and Backward Induction

Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang

International Conference on Machine Learning (ICML), 2021

Provably Efficient Causal Reinforcement Learning with Confounded Observational Data

Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

Advances in Neural Information Processing Systems (NeurIPS), 2021

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory

Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang

Advances in Neural Information Processing Systems (NeurIPS), 2020 (oral)

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, Qiaomin Xie

Advances in Neural Information Processing Systems (NeurIPS), 2020 (spotlight)

Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework

Wanxin Jin, Zhaoran Wang, Zhuoran Yang, Shaoshuai Mou

Advances in Neural Information Processing Systems (NeurIPS), 2020

Provably Efficient Exploration in Policy Optimization

Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang

International Conference on Machine Learning (ICML), 2020

Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation

and Correlated Equilibrium

Qiaomin Xie, Yudong Chen, Zhaoran Wang, Zhuoran Yang

Annual Conference on Learning Theory (COLT), 2020

Mathematics of Operations Research (MOR), 2022

Provably Efficient Reinforcement Learning with Linear Function Approximation

Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael Jordan

Annual Conference on Learning Theory (COLT), 2020

Mathematics of Operations Research (MOR), 2022

Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

International Conference on Learning Representations (ICLR), 2020

Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy

Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang

Advances in Neural Information Processing Systems (NeurIPS), 2019

Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima

Qi Cai, Zhuoran Yang, Jason Lee, Zhaoran Wang

Advances in Neural Information Processing Systems (NeurIPS), 2019

Mathematics of Operations Research (MOR), 2022

A Theoretical Analysis of Deep Q-Learning

Jianqing Fan, Zhaoran Wang, Yuchen Xie, Zhuoran Yang (alphabetical)

Learning for Dynamics and Control, 2019