Abstract: Information theory can shed light on the algorithm-independent limits of learning from data and serve as a design driver for new machine learning algorithms. In this talk, Dr. Calmon will discuss a set of flexible information-theoretic tools that can be used to (i) understand fairness and discrimination by machine learning models and (ii) characterize data representations learned by complex learning models. He will illustrate these techniques in both synthetic and real-world datasets, and discuss future research directions.
Refreshments will be served from 1-1:30pm on a first-come, first-served basis.
Speaker bio: Flavio P. Calmon is an Assistant Professor of Electrical Engineering at Harvard's John A. Paulson School of Engineering and Applied Sciences. Before joining Harvard, he was the inaugural data science for social good post-doctoral fellow at IBM Research in Yorktown Heights, New York. He received his Ph.D. in Electrical Engineering and Computer Science at MIT. His main research interests are information theory, inference, and statistics, with applications to privacy, fairness, machine learning, and content distribution.