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 [Full Publication List]

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang
Submitted, 2020
[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
Submitted, 2020
[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
Submitted, 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 I 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 D 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 order)
Submitted, 2019
[Arxiv]