Tengyang Xie

Hi! I'm a Ph.D. student in Computer Science at University of Illinois at Urbana-Champaign (UIUC), where I am fortunate to work with Nan Jiang. My research mainly focuses on interactive learning. Currently, I am working on two "opposite" directions: batch RL (i.e. off-policy RL) -- how to learn from the exploratory data solely, and exploration in RL -- how to explore the environment efficiently. I am also interested in unsupervised/self-supervised learning and the theory of deep neural networks.

Prior to UIUC, I was a Ph.D. student in Computer Science at University of Massachusetts Amherst, where I was introduced to RL and working with Phil Thomas. Before that, I obtained bachelor's degree in Physics from University of Science and Technology of China (USTC).

Publications

Batch Value-function Approximation with Only Realizability. [PDF, arXiv]
Tengyang Xie, Nan Jiang

Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. [PDF, arXiv]
Tengyang Xie, Nan Jiang
Conference on Uncertainty in Artificial Intelligence (UAI) 2020.

Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. [PDF, Poster, arXiv]
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Conference on Neural Information Processing Systems (NeurIPS) 2019.
Spotlight presentation at the NeurIPS 2018 Workshop on Causal Learning.

Provably Efficient Q-Learning with Low Switching Cost. [PDF, Poster, arXiv]
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Conference on Neural Information Processing Systems (NeurIPS) 2019.

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. [PDF, Poster, Link]
Tengyang Xie*, Bo Liu*, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon
Conference on Neural Information Processing Systems (NeurIPS) 2018.

Privacy Preserving Off-policy Evaluation. [PDF, arXiv]
Tengyang Xie, Philip S. Thomas, Gerome Miklau

(* indicates equal contribution or alphabetic ordering.)

Service

Conference Reviewer/Program Committee: NeurIPS (2020, 2019), ICML (2020, 2019), AISTATS (2020), AAAI (2020, 2019).
Journal Reviewer: Machine Learning Journal.
Program Committee for NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop.
Program Committee for ICML 2020 Theoretical Foundations of Reinforcement Learning Workshop.