Nan Jiang is an Assistant Professor of Computer Science at University of Illinois. He was a Postdoc Microsoft Research, and did his PhD at University of Michigan under Professor Satinder Singh.

Featured References

Reinforcement Learning: Theory and Algorithms
Alekh Agarwal Nan Jiang Sham M. Kakade
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen, Nan Jiang

Additional References

Towards a Unified Theory of State Abstraction for MDPs, Lihong Li, Thomas J. Walsh, Michael L. Littman
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning, Nan Jiang, Lihong Li
Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization, Nan Jiang, Jiawei Huang
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning, Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue

Errata

[Robin] I misspoke when I said in domain randomization we want the agent to "ignore" domain parameters. What I should have said is, we want the agent to perform well within some range of domain parameters, it should be robust with respect to domain parameters.