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Reading Seminar on Mathematics of Machine Learning
Recent advancements of machine learning methods lave led to breakthroughs in a wide range of applications. In spite of their empirical successes, the theoretical understanding of machine learning techniques are far from complete. New tools from mathematics and statistics have been showing their power in explaining the mystery and more will be emerging. The purpose of this reading seminar is threefold: First to discuss recent research works that lie in the interface of machine learning, mathematics, statistics and etc; second to expose new research questions and foster collaborations between different groups and even different departments; and third to create new research opportunities for junior researchers and graduate students. All faculty, VAPs, graduate students are welcome to join.
Date  Speaker  Title 

Fri. 2/17

Zhenjie Ren Université ParisDauphine 
Regularized Mean Field Optimization with Application to Neural Networks 
Fri. 2/24

Paul Hand Northeastern University 
Signal Recovery with Generative Priors 
Fri. 3/3

Bryn Elesedy Oxford 
Symmetry and Generalisation in Machine Learning 
Fri. 3/10

Robin Walters Northeastern University 
The Strengths and Limitations of Equivariant Neural Networks 
Fri. 3/24

Ziang Chen Duke University 
On Representing (MixedInteger) Linear Programs by Graph Neural Networks 
Fri. 3/31

Lénaïc Chizat EPFL 
A Tractable Barycenter for Probability Measures in Machine Learning 
Department of Mathematics and Statistics