Information Theory, Machine Learning and Statistics Seminar

Fall 2021

This term the Information Theory, Machine Learning and Statistics Seminar focuses on estimation
of divergence measures, generalization in machine learning, and related topics. The seminar consists
of a series of two-hours talks aimed at graduate students and researchers with a basic knowledge of
information theory and machine learning. Talks are unusually long to allow speakers to discuss their
results and proof techniques in detail.

Sessions take place every other Friday from 9am to 11am (CST) over Zoom.
(Access link:

To be added to the seminar mailing list, please send an email to

List of Speakers

Date Speaker Title
01/Oct Mario Diaz
Universidad Nacional Autónoma de México (UNAM)
Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation
15/Oct Wael Alghamdi
Harvard University (HU)
Measuring Information from Moments
29/Oct Amedeo R. Esposito
Swiss Federal Institute of Technology in Lausanne (EPFL)
Information Measures, Independence and Learning
12/Nov Canceled
26/nov Gergely Neu
Universitat Pompeu Fabra (UPF)
Generalization Bounds via Convex Analysis

Previous Schedules
[Spring 2021] [Fall 2020]

Last update: Nov. 22, 2021