Welcome
I am a final-year PhD student in the Statistics Department at the University of Michigan advised by Ambuj Tewari. Previously, I worked on recommendation systems and search engines as a machine learning engineer in the industry.
My research interests are broadly in design and analysis of algorithms for sequential decision making under uncertainty. I have worked on problems including non-stationary bandits, offline reinforcement learning, constrained reinforcement learning.
Outside of work, I enjoy running and playing the piano.
Publications
- Offline Constrained Reinforcement Learning with Arbitrary Data Distributions under Partial Coverage
Kihyuk Hong, Ambuj Tewari
In Preparation, 2025 - A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
Kihyuk Hong, Ambuj Tewari
In Submission, 2025 - Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs
Kihyuk Hong, Woojin Chae, Yufan Zhang, Dabeen Lee, Ambuj Tewari
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025 - Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025 - A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
Kihyuk Hong, Ambuj Tewari
International Conference on Machine Learning (ICML), 2024 - A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
Kihyuk Hong, Yuhang Li, Ambuj Tewari
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 - An Optimization-Based Algorithm for Non-Stationary Kernel Bandits without Prior Knowledge
Kihyuk Hong, Yuhang Li, Ambuj Tewari
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Talks
- Invited Talk, Stanford University. Computer Science Department. Feb 2025.
- University of Michigan. Statistics Department Seminar. Dec 2024.
- An algorithm for infinite-horizon average reward RL with linear MDPs. RL Theory Workshop. Jun 2024.
- Introduction to reinforcement learning, guest lecture for STATS 503, University of Michigan. May 2023.
Mentoring
- Yuhang Li, 06/2022 – 09/2023. Published 2 papers together.
- Yufan Zhang, 02/2024 – 08/2024. Published 2 papers together.
Teaching
- Teaching assistant, Advanced Statistical Learning (STATS 601), University of Michigan. Winter 2025
- Teaching assistant, Regression analysis (STATS 600), University of Michigan. Fall 2024
- Teaching assistant, Regression analysis (STATS 600), University of Michigan. Fall 2023
- Teaching assistant, Introduction to data science (STATS 206), University of Michigan. Fall 2021
- Teaching assistant, Survey sampling techniques (STATS 480), University of Michigan. Winter 2021
- Teaching assistant, Introduction to statistics and data analysis (STATS 250), University of Michigan. Fall 2020
Education
- PhD Statistics. University of Michigan. 2020 - current.
- MS Statistics. Stanford University. 2008 - 2010.
- BS Biomedical Engineering & Applied Math. Johns Hopkins University. 2004 - 2008.
Professional Experience
- Machine Learning Enigineer. Naver. Nov 2013 - Apr 2020.
- Software Engineer. Meta. Aug 2010 - Nov 2013.
Services
Reviewer
- International Conference on Artificial Intelligence and Statistics (AISTATS)
- Journal of Machine Learning Research (JMLR)
- Statistical Science
Organizer
- Department Representative, Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS), University of Michigan. 2023.