\dm_csml_event_details UCL ELLIS

Utilitarian Information Theory


Speaker

Stefano Ermon

Affiliation

Stanford University

Date

Friday, 25 March 2022

Time

16:00-17:00

Location

Zoom

Link

https://ucl.zoom.us/j/97245943682

Event series

Jump Trading/ELLIS CSML Seminar Series

Abstract

Shannon’s information theory, which lies at the foundation of AI and machine learning, provides a conceptual framework to characterize information in a mathematically rigorous sense. However, important computational aspects are not considered, as it does not account for how much information can actually be used by a computationally bounded decision maker. This limits its utility in several practical real-world scenarios. I will discuss generalizations of Shannon’s entropy, information and related divergences that account for how information will be used by a (computationally bounded) decision maker, as well as their applications in representation learning, structure learning, Bayesian optimization, fairness, among others.

Biography