Gauthier Gidel

Biography

I am an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core academic member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair.

Previously, I was awarded a Borealis AI Graduate Fellowship, worked at DeepMind and Element AI, and was a Long-Term Visitor at the Simons Institute at UC Berkeley.

My research interests lie at the intersection of game theory, optimization and machine learning.

Current Students

Alan Alan

Independent visiting researcher - UBC

Master's Research - Polytechnique Montréal

Principal supervisor :

Collaborating Alumni - Université de Montréal

Co-supervisor :

Collaborating researcher

Joey Bose

PhD - Université de Montréal

Damien Ferbach

PhD - Université de Montréal

Florian Golemo

Postdoctorate - McGill University

Principal supervisor :

PhD - McGill University

Principal supervisor :

Audrey Durand

Marco Jiralerspong

PhD - Université de Montréal

Website

Github

Zichu Liu

PhD - Université de Montréal

Co-supervisor :

Andjela Mladenovic

PhD - Université de Montréal

Website

Mehrnaz Mofakhami

Master's Research - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Leo Schwinn

Independent visiting researcher - Technical Univeristy of Munich

PhD - Université de Montréal

Github

Sophie Xhonneux

PhD - Université de Montréal

Co-supervisor :

Jian Tang

lpxhonneux@gmail.com

What Do Synaptic Weight Distributions Tell Us About Learning in the Brain ?

Blog Posts

June 13, 2024

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Read the article

Publications

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games

Waiss Azizian

We consider diﬀerentiable games where the goal is to ﬁnd a Nash equilibrium. The machine learning community has recently started using v… (see more)ariants of the gradient method ( GD ). Prime examples are extragradient ( EG ), the optimistic gradient method ( OG ) and consensus optimization ( CO ), which enjoy linear convergence in cases like bilinear games, where the standard GD fails. The full bene-ﬁts of theses relatively new methods are not known as there is no uniﬁed analysis for both strongly monotone and bilinear games. We provide new analyses of the EG ’s local and global convergence properties and use is to get a tighter global convergence rate for OG and CO . Our analysis covers the whole range of settings between bilinear and strongly monotone games. It reveals that these methods converges via diﬀerent mechanisms at these extremes; in between, it exploits the most favorable mechanism for the given problem. We then prove that EG achieves the optimal rate for a wide class of algorithms with any number of extrapolations. Our tight analysis of EG ’s convergence rate in games shows that, unlike in convex minimization, EG may be much faster than GD .

2020-01-01

International Conference on Artificial Intelligence and Statistics (published)

dblp.uni-trier.de

Negative Momentum for Improved Game Dynamics

Reyhane Askari Hemmat

Mohammad Pezeshki

Gabriel Huang

Rémi LE PRIOL

Games generalize the single-objective optimization paradigm by introducing different objective functions for different players. Differentiab… (see more)le games often proceed by simultaneous or alternating gradient updates. In machine learning, games are gaining new importance through formulations like generative adversarial networks (GANs) and actor-critic systems. However, compared to single-objective optimization, game dynamics are more complex and less understood. In this paper, we analyze gradient-based methods with momentum on simple games. We prove that alternating updates are more stable than simultaneous updates. Next, we show both theoretically and empirically that alternating gradient updates with a negative momentum term achieves convergence in a difficult toy adversarial problem, but also on the notoriously difficult to train saturating GANs.

2019-04-11

Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Negative Momentum for Improved Game Dynamics

Reyhane Askari Hemmat

Mohammad Pezeshki

Gabriel Huang

Rémi LE PRIOL

2018-07-12

ArXiv (preprint)

Frank-Wolfe Splitting via Augmented Lagrangian Method

Fabian Pedregosa

Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than mini… (see more)mizing it over each individual constraint set. While traditional methods such as Frank-Wolfe (FW) or proximal gradient descent assume access to a linear or quadratic oracle on the intersection, splitting techniques take advantage of the structure of each sets, and only require access to the oracle on the individual constraints. In this work, we develop and analyze the Frank-Wolfe Augmented Lagrangian (FW-AL) algorithm, a method for minimizing a smooth function over convex compact sets related by a "linear consistency" constraint that only requires access to a linear minimization oracle over the individual constraints. It is based on the Augmented Lagrangian Method (ALM), also known as Method of Multipliers, but unlike most existing splitting methods, it only requires access to linear (instead of quadratic) minimization oracles. We use recent advances in the analysis of Frank-Wolfe and the alternating direction method of multipliers algorithms to prove a sublinear convergence rate for FW-AL over general convex compact sets and a linear convergence rate for polytopes.

2018-03-31

ArXiv (preprint)

Frank-Wolfe Splitting via Augmented Lagrangian Method

Fabian Pedregosa

2018-01-01

AISTATS (published)

proceedings.mlr.press