I am Bingshan Hu. I defended my thesis in Sep 2021 advised by Nishant Mehta from University of Victoria. I am working on the theoretical side of machine learning, particularly, Thompson Sampling-based learning algorithms for sequential decision making problems. My Google Scholar page can be found in Bingshan's Google Scholar.
Starting from Nov 2023 , I am a post-doc in UBC Data Science Institute co-hosted by Danica Sutherland , Trevor Campbell, and Mathias Lecuyer all from University of British Columbia. From Nov 2021 to Oct 2023, I was an Amii post-doc co-hosted by Nidhi Hegde from University of Alberta, and Mark Schmidt and Mathias Lecuyer from University of British Columbia.
Prior to coming to Canada for my PhD study, I was a 3GPP delegate of SONY. I filed around 20 patents about how to use unlicensed spectrum from cellular operator's perspective and Device-to-Device communications. My full patent list can be found in Bingshan's Patents.
Emails: bingsha1@cs.ubc.ca asonymous@yahoo.com
Sep 2016 - Sep 2021: University of Victoria (UVic), Victoria, Canada
Ph.D. in Department of Computer Science
Thesis topic: Bandit algorithms with graphical feedback models and privacy awareness
Thesis nominated for Governor General’s Gold Medal by Department of Computer Science
Sep 2006 – Mar 2013: Beijing University of Posts and Telecommunications (BUPT), Beijing, China
B.S. and M.S. in Communication Engineering
Publications and preprints
(Near)-Optimal Algorithms For Differentially Private Online Learning in a Stochastic Environment, Bingshan Hu, Zhiming Huang, Nishant Mehta, and Nidhi Hegde, Under-review, 2024
From 6235149080811616882909238708 to 29: Vanilla Thompson Sampling Revisited, Bingshan Hu, Tianyue H. Zhang, OPT NeurIPS 2023 Workshop, PDF
Differentially Private Algorithms for Efficient Online Matroid Optimization, Kushagra Chandak, Bingshan Hu, and Nidhi Hegde, CoLLAs 2023, PDF
Optimistic Thompson Sampling-based Algorithms for Episodic Reinforcement Learning, Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, and Mark Schmidt, UAI 2023, PDF
Near-Optimal Thompson Sampling-based Algorithms for Differentially Private Stochastic Bandits, Bingshan Hu and Nidhi Hegde, UAI 2022, PDF
Problem-Dependent Regret Bounds for Online Learning with Feedback Graphs, Bingshan Hu, Nishant Mehta, and Jianping Pan, UAI 2019, PDF
Poster: Multi-agent Combinatorial Bandits with Moving Arms, Zhiming Huang, Bingshan Hu, and Jianping Pan, IEEE ICDCS 2021, Best Poster Award
Caching by User Preference with Delayed Feedback for Heterogeneous Cellular Networks, Zhiming Huang, Bingshan Hu, and Jianping Pan, IEEE Transactions on Wireless Communications (TWC), 2020
Intelligent Caching Algorithms in Heterogeneous Wireless Networks with Uncertainty, Bingshan Hu, Yunjin Chen, Zhiming Huang, Nishant Mehta, and Jianping Pan, IEEE ICDCS 2019
Intelligent Caching in Dense Small-cell Networks with Limited External Resources, Bingshan Hu, Maryam Tanha, Dawood Sajjadi, and Jianping Pan, IEEE LCN 2018