Michael Woodroofe Lecture Series: Tze Leung Lai, Professor, Department of Statistics, Stanford University

In the past five years, multi-armed bandits with covariates, also called "contextual bandits" in machine learning, have become an active area of research in data science, stochastic optimization, and statistical modeling because of their applications to the development of personalized strategies in translational medicine and in recommender systems for web-based marketing and electronic business. After a brief review of classical (context-free) bandit theory, we describe a corresponding theory, covering both parametric and nonparametric approaches, for contextual bandits and illustrate their applications to personalized strategies.