Martin Wainwright
Professor of Electrical Engineering and Computer Sciences at MIT

Banatao Auditorium | 310 Sutardja Dai Hall
Friday, April 12, 2024 at 2pm

In many modern uses of predictive methods, there can be shifts between the distributional properties of training data compared to the test data.  Such mismatches can cause dramatic reductions in accuracy that remain mysterious. How to find practical procedures that mitigate such effects in an optimal way?  In this talk, we discuss the fundamental limits of problems with covariate shift, and simple procedures that achieve these fundamental limits.  Our talk covers both the challenges of covariate shift in non-parametric regression, and also for semi-parametric problems that arise from causal inference and off-policy evaluation.

Based on joint works with: Peter Bartlett, Peng Ding, Cong Ma, Wenlong
Mou, Reese Pathak and Lin Xiao.

Speaker Bio

Martin Wainwright is the Cecil H. Green Professor in Electrical Engineering and Computer Science and Mathematics at MIT, and affiliated with the Laboratory for Information and Decision Systems and Statistics and Data Science Center. He joined the MIT faculty in July 2022 after spending 20 years in Statistics and EECS at the University of California at Berkeley.

Martin is broadly interested in statistics, machine learning, information theory and optimization.  His work has been recognized by various awards, among them the COPSS Presidents’ Award from the Joint Statistical Societies, the David Blackwell Award from the Institute of Mathematical Statistics, a Section Lecturer from the International Congress of Mathematicians, and a Sloan Foundation Fellowship.  He has co-authored several books, including on graphical models, sparse statistical models, and high-dimensional statistics.