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, such as reinforcement learning, contextual bandits, and causal inference. Currently, I am working on two "opposite" directions: batch RL (a.k.a. off-policy/offline 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
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Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. [PDF, arXiv]
Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie
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A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. [PDF, arXiv]
Philip Amortila*, Nan Jiang*, Tengyang Xie*
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Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. [PDF, arXiv]
Tengyang Xie, Nan Jiang
Conference on Uncertainty in Artificial Intelligence (UAI) 2020.
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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.
© 2020 Tengyang Xie
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