Fang Kong (Shanghai Jiao Tong University)

Time:

• 16:20-17:20 (Time in Beijing)
• November 29, 2023 (Wednesday)

Venue:

518, Research Building 4

Abstract:

The multi-armed bandits (MAB) problem is a classic online learning framework that characterizes the learning process of an agent within an unfamiliar environment. Serving as a basic framework for interactive machine learning, it finds broad practical applications across diverse domains, including recommender systems, online experimentation, game-playing algorithms such as AlphaGo, and combinatorial optimization problems such as SAT solvers. Despite its foundational significance, when the arm space exhibits considerable size, traditional MAB algorithms face the curse of dimensionality. To make the algorithm applicable to large-scale environments and improve learning efficiency, the investigation of side information is widely studied in the literature. In this presentation, I will introduce our research efforts in developing efficient algorithms enriched with graph and feature information and show the corresponding theoretical analysis. Additionally, I will introduce our research progress on tackling general combinatorial action spaces by mining side information on involved arms and multi-agent systems by effectively utilizing information on agents’ observations.

Speaker Bio:

Fang Kong is currently a Ph.D. candidate in the John Hopcroft Center for Computer Science, Shanghai Jiao Tong University. She is also a member in Wu Honor Ph.D. Class in Artificial Intelligence. Fang received her B.S. degree from Shandong University in 2020. Her current research interests focus on bandit algorithms and reinforcement learning theory. Her research work has been published in top conferences of machine learning and theoretical computer science such as SODA, COLT (the first from SJTU), ICML and NeurIPS. She also serves as a reviewer for mainstream machine learning conferences. During her Ph.D. studies, Fang has been a research intern or visiting student of the Chinese University of Hong Kong, Tencent, Microsoft Research Asia, and Alibaba Damo Academy.