Events

CS Colloquium: How to handle Biased Data and Multiple Agents in Machine Learning

Speaker: Manolis Zampetakis

Location: On-Line
Videoconference link: https://nyu.zoom.us/j/98887949042

Date: Friday, March 11, 2022

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
objective optimization.