157 citations · 529 across the 78 of their papers we have counts for
9 papers · 1 filter
Stochastic Extragradient: General Analysis and Improved Rates
Eduard Gorbunov, Hugo Berard, Gauthier Gidel +1
The Stochastic Extragradient (SEG) method is one of the most popular algorithms for solving min-max optimization and variational inequalities problems (VIP) appearing in various ma…
Generating Diverse Realistic Laughter for Interactive Art
M. Mehdi Afsar, Eric Park, Étienne Paquette +3
We propose an interactive art project to make those rendered invisible by the COVID-19 crisis and its concomitant solitude reappear through the welcome melody of laughter, and conn…
Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks
Manuela Girotti, Ioannis Mitliagkas, Gauthier Gidel
We theoretically analyze the Feedback Alignment (FA) algorithm, an efficient alternative to backpropagation for training neural networks. We provide convergence guarantees with rat…
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu +5
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of…
Extragradient Method: Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity
Eduard Gorbunov, Nicolas Loizou, Gauthier Gidel
Extragradient method (EG) (Korpelevich, 1976) is one of the most popular methods for solving saddle point and variational inequalities problems (VIP). Despite its long history and…
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
Chris Junchi Li, Yaodong Yu, Nicolas Loizou +4
We study the stochastic bilinear minimax optimization problem, presenting an analysis of the same-sample Stochastic ExtraGradient (SEG) method with constant step size, and presenti…
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
Nicolas Loizou, Hugo Berard, Gauthier Gidel +2
Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic conse…
A single gradient step finds adversarial examples on random two-layers neural networks
Sébastien Bubeck, Yeshwanth Cherapanamjeri, Gauthier Gidel +1
Daniely and Schacham recently showed that gradient descent finds adversarial examples on random undercomplete two-layers ReLU neural networks. The term "undercomplete" refers to th…
Online Adversarial Attacks
Andjela Mladenovic, Avishek Joey Bose, Hugo Berard +4
Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we forma…