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
Universidad Nacional Autónoma de México
Generalization in Machine Learning via (Conditional) Mutual Information
Arizona State University
Synthesizing Classification-Calibration and Rademacher Complexity Generalization with alpha-loss
University of Toronto
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
KTH Royal Institute of Technology
Tighter Expected Generalization Error Bounds via Wasserstein Distance
On Discrete Analogs of the Entropy Power Inequality
Max Planck Institute for the Physics of Complex Systems
A Phase Transition in Terms of the Shannon Entropy
Last update: May 22, 2021