Speaker |
Badr-Eddine Chérief-Abdellatif |
|---|---|
Affiliation |
University of Oxford |
Date |
Friday, 10 June 2022 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
In this talk, we will study the properties of a minimum distance estimator based on the Maximum Mean Discrepancy (MMD). We will show that this estimator is universal in the i.i.d. setting, even in case of misspecification, it converges to the best approximation of the data generation process in the model, without any assumption on this model. We will also show that these results remain valid when the data are not independent, but rather satisfy a weak-dependence condition. This condition is based on a new dependence coefficient, which is itself defined using the MMD. We will argue with examples that this new notion of dependence is in fact quite general. |
Biography |
Badr-Eddine Chérief-Abdellatif currently holds a postdoctoral research position in the Department of Statistics at the University of Oxford, working with Arnaud Doucet. Prior to that, he received a PhD in statistics from Institut Polytechnique de Paris prepared at CREST (Center for Research in Economics and Statistics), Paris, under the supervision of Pierre Alquier, currently research scientist at RIKEN AIP in Tokyo. His research covers the fundamental aspects of statistics and machine learning, with a particular focus on the development of tractable and efficient learning methods, and on understanding their statistical properties and their ability to generalize. He is particularly interested in variational inference and in PAC-Bayes theory, and more generally in robust statistics, high-dimensional statistics, online learning and optimization. |