Spring 2021

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 MéxicoGeneralization in Machine Learning via (Conditional) Mutual Information
19/March
Tyler Sypherd

Arizona State UniversitySynthesizing Classification-Calibration and Rademacher Complexity Generalization with alpha-loss
16/April
Mahdi Haghifam

University of TorontoSharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
30/April
Borja Rodríguez

KTH Royal Institute of TechnologyTighter Expected Generalization Error Bounds via Wasserstein Distance
14/May
James Melbourne

CIMATOn Discrete Analogs of the Entropy Power Inequality
28/May
Alvaro Díaz

Max Planck Institute for the Physics of Complex SystemsA Phase Transition in Terms of the Shannon Entropy

Last update: May 22, 2021