Course: STAT 37786
Title: Topics in Learning Under Distribution Shifts
Instructor(s): Cong Ma
Class Schedule: Sec 1: MW 11:00 AM-12:20 PM in Jones 226
Description: Traditional supervised learning assumes that the training and testing distributions are the same. Such a no-distribution-shift assumption, however, is frequently violated in practice. In this course, we survey topics in machine learning in which distribution shifts naturally arise. Possible topics include supervised learning with covariate shift, off-policy evaluation in reinforcement learning, and offline reinforcement learning.