Title: Reveal Planning Capability of Autoregressive Learning in Large Language Models
Abstract: Search and planning are fundamental constructs of human intelligence, involved in almost every aspect of our daily lives, from completing tasks at work to organizing trips, to seeking mathematical proofs of theorems, and more. Studying the planning capabilities of large language models (LLMs) can help us understand the differences in the decision-making processes between humans and artificial...
Title: Reinforcement Learning Meets Bilevel Optimization: Learning Leader-Follower Games with Sample Efficiency
Abstract: In this talk, I will introduce methods that modify the optimism principle for reinforcement learning in leader-follower games, especially when the follower's reward function is unknown. Such problems generally face statistical challenges due to the ill-posed nature of the best response function. I will discuss two cases that overcome these challenges. The first involves a fully rational follower with a...