Zhaoran Wang

I am an assistant professor in the Departments of Industrial Engineering & Management Sciences and Computer Science (by courtesy) at Northwestern University (since 2018). I am affiliated with the Centers for Deep Learning and Optimization & Statistical Learning.

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:
  • making deep reinforcement learning more efficient, both computationally and statistically, in a principled manner to enable its applications in critical domains;
  • 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 Recent Papers

Is Pessimism Provably Efficient for Offline RL?
Ying Jin, Zhuoran Yang, Zhaoran Wang
International Conference on Machine Learning (ICML), 2021
[Arxiv]
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
[Arxiv] [Github]
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)
[Arxiv]
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
Submitted, 2020
[Arxiv]
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)
[Arxiv]
A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application
to Actor-Critic

Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang
Submitted, 2020
[Arxiv]
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
[Arxiv] [Demo]
Provably Efficient Exploration in Policy Optimization
Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
International Conference on Machine Learning (ICML), 2020
[Arxiv]
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
[Arxiv]
Provably Efficient Reinforcement Learning with Linear Function Approximation
Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael Jordan
Annual Conference on Learning Theory (COLT), 2020
[Arxiv]
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
[Arxiv]
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
[Arxiv]
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
[Arxiv]
A Theoretical Analysis of Deep Q-Learning
Jianqing Fan, Zhaoran Wang, Yuchen Xie, Zhuoran Yang (alphabetical)
Submitted, 2019
[Arxiv]
Acknowledgement: National Science Foundation (Awards 2048075, 2008827, 2015568, 1934931),
Simons Institute (Theory of Reinforcement Learning), Amazon, J.P. Morgan, Two Sigma