Information Theory, Machine Learning and Statistics Seminar

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: https://cuaed-unam.zoom.us/j/83608728443)

To be added to the seminar mailing list, please send an email to mario.diaz@sigma.iimas.unam.mx

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	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