题目：Introduction to Multi-armed Bandits

主讲人：伊利诺伊大学芝加哥分校 周文心副教授

主持人：西南财经大学统计学院 常晋源教授

时间：2024年6月26日（周三）下午14:00-17:00

    2024年6月27日（周四）下午14:00-17:00

    2024年7月2日（周二）下午14:00-17:00

    2024年7月3日（周三）下午14:00-17:00

地点：西南财经大学光华校区光华裙楼2303

报告摘要：

Multi-armed bandits are a simple but powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. In these talks, we provide an introductory treatment of the subject. Each chapter tackles a particular line of work, offering a self-contained, teachable technical introduction and a brief review of further developments. To begin with, we discuss a spectrum of decision-making problems, online learning, and prediction. Then, we describe some of the fundamental algorithms for multi-armed bandits. The second chapter concerns contextual bandits, focusing on stochastic linear bandits. In chapter three, we consider the stochastic sparse linear bandit problem, where only a sparse subset of context features affects the expected reward function. We will review some representative and very recent works in this direction and discuss some open questions.

主讲人简介：

Wen-Xin Zhou is an Associate Professor in the Department of Information and Decision Sciences at the College of Business Administration, University of Illinois at Chicago. From 2017 to 2023, he was a faculty member in the Department of Mathematics at the University of California, San Diego. His research interests include high-dimensional statistics, robust learning for heavy-tailed data, nonparametric statistics, neural networks and deep learning, quantile regression methods and beyond.