Title:

Stochastic optimization under distributional shifts

Abstract:

Learning problems commonly exhibit an interesting feedback

mechanism wherein the population data reacts to decision makers'

actions. This is the case for example when members of the population

respond to a deployed classifier by manipulating their features so as

to improve the likelihood of being positively labeled. In this way,

the population is manipulating the learning process by distorting the

data distribution that is accessible to the learner. In this talk, I will present some recent modelling frameworks and algorithms for dynamic problems of this type, rooted in stochastic optimization and game theory.


Joint work with Evan Faulkner (UW), Maryam Fazel (UW), Adhyyan Narang

(UW), Lillian J. Ratliff (UW), Lin Xiao (Facebook AI)

Bio:

Dmitriy Drusvyatskiy received his PhD from the Operations

Research and Information Engineering department at Cornell University

in 2013, followed by a post doctoral appointment in the Combinatorics

and Optimization department at Waterloo, 2013-2014. He joined the

Mathematics department at University of Washington as an Assistant

Professor in 2014, and was promoted to an Associate Professor in 2019.

Dmitriy's research broadly focuses on designing and analyzing

algorithms for large-scale optimization problems, primarily motivated

by applications in data science. Dmitriy has received a number of

awards, including the Air Force Office of Scientific Research (AFOSR)

Young Investigator Program (YIP) Award, NSF CAREER, INFORMS

Optimization Society Young Researcher Prize 2019, and finalist

citations for the Tucker Prize 2015 and the Young Researcher Best

Paper Prize at ICCOPT 2019. Dmitriy is currently a co-PI of the NSF

funded Transdisciplinary Research in Principles of Data Science

(TRIPODS) institute at University of Washington.


Research currently supported by NSF CAREER DMS 1651851 and NSF CCF 1740551.