Portrait of Gauthier Gidel

Gauthier Gidel

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Generative Models
Machine Learning Theory
Optimization
Reinforcement Learning

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

Master's Research - Université de Montréal
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Independent visiting researcher - N/A
PhD - Université de Montréal
Co-supervisor :
Research Intern - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :
Collaborating researcher - Université de Montréal
Co-supervisor :
Collaborating researcher - Université de Montréal
Independent visiting researcher - Technical Univeristy of Munich
Research Intern - Université de Montréal
Postdoctorate - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
Collaborating Alumni - N/A

Publications

Frank-Wolfe Splitting via Augmented Lagrangian Method
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.