A wide range of operations problems are built on an underlying probabilistic model. However, estimation error always exists when learning from these models, which can lead to misleading decision-making. Moreover, these models are inherently mis-specified to a certain degree, which calls for robust learning and policies for these operations problems. In this talk, we will discuss robust machine learning and its applications to operations problems.
In the first part of the talk, we discuss a non-parametric approach for robust learning in sequential stochastic assignment problem. The second part of the talk considers the dynamic assortment optimization problem under the multinomial logit model (MNL) with unknown utility parameters. Based on an online eps-contamination modelling of customers’ purchase behavior, we develop a rate-optimal robust online assortment optimization policy via an active elimination strategy.
Xi Chen is an assistant professor at Stern School of Business at New York University, who is also an affiliated professor to Computer Science and Center for Data Science. Before that, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley. He obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University (CMU), M.Sc. from Tepper School of Business at CMU. He studies high-dimensional statistical learning, online learning, large-scale stochastic optimization, and applications to operations management. He has published more than 20 journal articles in statistics, machine learning, and operations, and nearly 30 top machine learning peer-reviewed conference proceedings. He received NSF Career Award, ICSA Outstanding Young Researcher Award, Faculty Research Awards from Google, Adobe, Alibaba, and Bloomberg, and was featured in Forbes list of “30 Under30 in Science”.