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Siwei Wang (汪思为)

Postdoc
Department of Computer Science and Technology,
Tsinghua University,
1-508 FIT Building, Tsinghua University,
Beijing, China, 100084,
Email: wangsw2020@mail.tsinghua.edu.cn

About me

I am now a postdoc at Tsinghua University hosted by Prof. Jun Zhu.

I received my doctor degree in Computer Science from the IIIS, Tsinghua University in June 2020. My thesis advisor was Prof. Longbo Huang.

I received my bachelor degree in Computer Science from IIIS, Tsinghua University in June 2015.

Research

I mainly focus on theoretical problems in artificial intelligence and machine learning, especially the online learning problems, e.g., bandit problems and online reinforcement learning problems.

Internship

Theory Group, Microsoft Research Asia, Beijing, June 2016 - September 2016.

Research Visit

Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong, Hong Kong, June 2019 - September 2019.

Publications

  1. Siwei Wang, Haoyun Wang and Longbo Huang, “Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback”, Proceedings of the Thirty-fifth AAAI Conference on Artificial Intelligence, February 2021.

  2. Yihan Du, Siwei Wang and Longbo Huang, “A One-Size-Fits-All Solution to Conservative Bandit Problems”, Proceedings of the Thirty-fifth AAAI Conference on Artificial Intelligence, February 2021.

  3. Siwei Wang, Longbo Huang and John C. S. Lui, “Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits”, Proceedings of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), December 2020.

  4. Yihan Du, Siwei Wang and Longbo Huang, “Dueling Bandits: From Two-dueling to Multi-dueling”, International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), May 2020.

  5. Siwei Wang and Longbo Huang, “Multi-armed Bandits with Compensation”, Proceedings of the Thirty-second Conference on Neural Information Processing Systems (NeurIPS), December 2018. (pdf)

  6. Siwei Wang and Wei Chen, “Thompson Sampling for Combinatorial Semi-Bandits”, Proceedings of the Thirty-fifth International Conference on Machine Learning (ICML), July 2018. (pdf)

  7. Yifeng Teng, Shenghao Yang, Siwei Wang and Mingfei Zhao, “Tight Bound on Randomness for Violating the CHSH Inequality”, IEEE Transactions on Information Theory, April 2016. (pdf)