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 |
DeepMind/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 |