How to handle Biased Data and Multiple Agents in Machine Learning?
Speaker: Manolis Zampetakis, University of California, Berkeley
Location: 60 Fifth Avenue 150
Date: March 11, 2022, 11 a.m.
Host: Joan Bruna
Modern machine learning (ML) methods commonly postulate strong
assumptions such as: (1) access to data that adequately captures the
application environment, (2) the goal is to optimize the objective
function of a single agent, assuming that the application environment is
isolated and is not affected by the outcome chosen by the ML system. In
this talk I will present methods with theoretical guarantees that are
applicable in the absence of (1) and (2) as well as corresponding
fundamental lower bounds. In the context of (1) I will focus on how to
deal with truncation and self-selection bias and in the context of (2) I
will present a foundational comparison between two-objective and single
Manolis Zampetakis is currently a post-doctoral researcher at the EECS
Department of UC Berkeley working with Michael Jordan. He received his
PhD from the EECS Department at MIT where he was advised by Constantinos
Daskalakis. He has been awarded the Google PhD Fellowship and the ACM
SIGEcom Doctoral Dissertation Award. He works on the foundations of
machine learning (ML), statistics, and data science, with focus on
statistical analysis from systematically biased data, optimization
methods for multi-agent environments, and convergence properties of
popular heuristic methods.
In-person attendance only available to those with active NYU ID cards.