Information Theory, Machine Learning and Statistics Seminar

This term the Information Theory, Machine Learning and Statistics Seminar focuses on generaliza-
tion in machine learning and divergence measures, some of the topics studied last term. The seminar
consists of a series of two-hours talks aimed to graduate students and researchers with a basic know-
ledge of information theory and machine learning (e.g., as covered in the mini-courses last term).

The seminar runs in a bi-weekly format, taking place every other Friday from 9am to 11am (CDT).
The seminar sessions are transmitted using Zoom, please request the access link to Mario Diaz.

List of Speakers

Date	Speaker	Title
05/March	Mario Diaz Universidad Nacional Autónoma de México	Generalization in Machine Learning via (Conditional) Mutual Information
19/March	Tyler Sypherd Arizona State University	Synthesizing Classification-Calibration and Rademacher Complexity Generalization with alpha-loss
16/April	Mahdi Haghifam University of Toronto	Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
30/April	Borja Rodríguez KTH Royal Institute of Technology	Tighter Expected Generalization Error Bounds via Wasserstein Distance
14/May	James Melbourne CIMAT	On Discrete Analogs of the Entropy Power Inequality
28/May	Alvaro Díaz Max Planck Institute for the Physics of Complex Systems	A Phase Transition in Terms of the Shannon Entropy

Last update: May 22, 2021