Talk by Prof. Simon Lacoste-Julien (Université de Montréal, Canada)

Speaker:
Prof. Simon Lacoste-Julien (Université de Montréal, Canada)

Title: New Perspectives on Generative Adversarial Networks

Abstract:
Generative Adversarial Networks (GANs) are a popular generative
modeling approach known for producing appealing samples, but their
theoretical properties are not yet fully understood, and they are
notably difficult to train. In the first part of this talk, I will
provide some insights on why GANs are a more meaningful framework to
model high dimensional data like images than the more traditional
maximum likelihood approach, interpreting them as "parametric
adversarial divergences" and rooting the analysis with statistical
decision theory. In the second part of the talk, I will address the
difficulty of training GANs from the optimization perspective by
importing tools from the mathematical programming literature. I will
survey the "variational inequality" framework which contains most
formulations of GANs introduced so far, and present theoretical and
empirical results on adapting the standard methods (such as the
extragradient method) from this literature to the training of GANs.

The talk is based on the following two papers:
"Parametric Adversarial Divergences are Good Task Losses for
Generative Modeling",
G. Huang, H. Berard, A. Touati, G. Gidel, P. Vincent, S. Lacoste-Julien
https://arxiv.org/abs/1708.02511

"A Variational Inequality Perspective on GANs",
G. Gidel, H. Berard, P. Vincent, S. Lacoste-Julien
https://arxiv.org/abs/1802.10551

Bio:
Simon Lacoste-Julien is a CIFAR fellow and an assistant professor at
MILA and DIRO from Université de Montréal. His research interests are
machine learning and applied math, with applications to computer
vision and natural language processing. He obtained a B.Sc. in math.,
physics and computer science from McGill, a PhD in computer science
from UC Berkeley and a post-doc from the University of Cambridge. He
spent a few years as a research faculty at INRIA and École normale
supérieure in Paris before coming back to his roots in Montreal in
2016 to answer the call from Yoshua Bengio in growing the Montreal AI
ecosystem.