Statistical Problems Related to (LLM) Alignment and Preference Learning
Project #1. Bandit/Statistical Problems related to Reward-based RLHF
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
- Accepted to NeurIPS 2024.
- Accepted to ICML 2024 Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET) as oral.
- Joint work with Se-Young Yun (KAIST AI) and Kwang-Sung Jun (Univ. of Arizona CS).
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
- Accepted to AISTATS 2024.
- Joint work with Se-Young Yun (KAIST AI) and Kwang-Sung Jun (Univ. of Arizona CS).
Project #2. Bandit/Statistical Problems related to General Preference Learning
GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
- Accepted to ICML 2025 Spotlight.
- Joint work with Kyoungseok Jang (CAU AI), Kwang-Sung Jun (Univ. of Arizona CS), Milan Vojnović (LSE Stat), and Se-Young Yun (KAIST AI).
Junghyun Lee
PhD Student
PhD student at GSAI, KAIST, jointly advised by Profs. Se-Young Yun and Chulhee Yun. Research focuses on interactive machine learning, particularly at the intersection of RLHF and preference learning, and statistical analyses of large networks, with an emphasis on community detection. Broadly interested in mathematical and theoretical AI and related problems in mathematics.
