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. Specific areas of interest include: non-stationary bandits, offline reinforcement learning, constrained reinforcement learning. Currently, I am interested in preference learning and preference aggregation in the context of RLHF.

Publications

1. Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
   Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee
   In Submission, 2024
2. 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
   In Submission, 2024
3. A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
   Kihyuk Hong, Ambuj Tewari
   International Conference on Machine Learning (ICML), 2024
4. 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
5. 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

1. An algorithm for infinite-horizon average reward RL with linear MDPs. RL Theory Workshop. Jun 2024.
2. 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, 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.
• MS Statistics. Stanford University.
• BS Biomedical Engineering & Applied Math.

Professional Experience

• Machine Learning Enigineer. Naver. Nov2013 - Apr 2020.
• Software Engineer. Meta. Aug 2010 - Nov 2013.

Services

Conference Reviewer

• International Conference on Artificial Intelligence and Statistics (AISTATS): 2022, 2023, 2024

Journal Reviewer

• Journal of Machine Learning Research (JMLR) : 1 time.
• Statistical Science : 2 times.

Organizer

• Department Representative, Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS), University of Michigan. 2023.