I am an assistant professor at Université de Montréal (UdeM) at DIRO and a core faculty member of Mila. I graduated my Ph.D. under the supervision of Simon Lacoste-Julien. During my Ph.D., I have been an intern at Sierra, ElementAI and DeepMind.
Please read this page before sending me any email (otherwise you may get no answer).
37
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Beyond L1: Faster and Better Sparse Models with skglm By Quentin Bertrand, Quentin Klopfenstein, Pierre-Antoine Bannier, Gauthier Gidel, and Mathurin Massias. Preprint 2022
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36
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Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities By Eduard Gorbunov, Adrien Taylor, and Gauthier Gidel. Preprint 2022
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35
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Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top By Eduard Gorbunov, Samuel Horváth, Peter Richtárik, and Gauthier Gidel. Preprint 2022
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34
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Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise By Eduard Gorbunov, Marina Danilova, David Dobre, Pavel Dvurechensky, Alexander Gasnikov, and Gauthier Gidel. Preprint 2022
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33
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A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis By Damien Ferbach, Christos Tsirigotis, Gauthier Gidel, and Joey Bose. Preprint 2022
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32
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On the Limitations of Elo: Real-World Games, are Transitive, not Additive By Quentin Bertrand, Wojciech Marian Czarnecki, and Gauthier Gidel. Preprint 2022
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31
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Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization By Simon S Du, Gauthier Gidel, Michael I Jordan, Chris Junchi Li. preprint 2022
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30
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Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime By Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, and Courtney Paquette. ICML 2022
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29
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Stochastic extragradient: General analysis and improved rates By Eduard Gorbunov, Hugo Berard, Gauthier Gidel, and Nicolas Loizou. AISTATS 2022
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28
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Extragradient method: O(1/K) last-iterate convergence for monotone variational inequalities and connections with cocoercivity By Eduard Gorbunov, Nicolas Loizou, and Gauthier Gidel. AISTATS 2022
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27
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Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks By Manuela Girotti, Ioannis Mitliagkas and Gauthier Gidel. Unpublished. Presented at the Beyond first-order methods in ML systems ICML2021 workshop.
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26
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On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging By Chris Junchi Li*, Yaodong Yu* , Nicolas Loizou, Gauthier Gidel, Yi Ma, Nicolas Le Roux and Michael I Jordan. *Equal contributions. AISTATS 2022
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25
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Generalized Natural Gradient Flows in Hidden Convex-Concave Games and GANs By Andjela Mladenovic*, Iosif Sakos, Georgios Piliouras, and Gauthier Gidel. ICLR 2022
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24
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Online Adversarial Attacks By Andjela Mladenovic*, Joey Bose*,Hugo Berard*, Will Hamilton, Simon Lacoste-Julien, Pascal Vincent, and Gauthier Gidel. *Equal contributions. ICLR 2022
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23
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Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity By Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas and Simon Lacoste-Julien. NeurIPS 2021.
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22
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Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity By Marta Garnelo, Wojciech Czarnecki , Siqi Liu, Dhruva Tirumala, Junhyuk Oh, Gauthier Gidel, Hado van Hassel and David Balduzzi. *Equal contributions. AAMAS 2021 (extended-abstract).
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21
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A single gradient step finds adversarial examples on random two-layers neural networks By Sebastien Bubeck, Yeshwanth Cherapanamjeri , Gauthier Gidel and Rémi Tachet des Combes. Authors in aphabetical order. NeurIPS 2021.
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20
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Multi-player games in the era of machine learning
Gauthier Gidel.
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19
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Adversarial Example Games By Joey Bose*, G.G.*, Hugo Berard*, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien and Will Hamilton. *Equal contributions. NeurIPS 2020.
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18
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Real World Games Look Like Spinning Tops By Wojciech Czarnecki, Gauthier Gidel, Brendan Tracey, Karl Tuyls, Shayegan Omidshafiei and David Balduzzi and Max Jaderberg. NeurIPS 2020.
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17
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A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets By Gauthier Gidel, Wojciech Czarnecki, Yoram Bachrach , Marta Garnelo and David Balduzzi. AISTATS 2021.
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16
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Accelerating Smooth Games by Manipulating Spectral Shapes By Waïss Azizian, Damien Scieur,Ioannis Mitliagkas, Simon Lacoste-Julien and Gauthier Gidel. AISTATS 2020.
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15
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Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent By James P. Bailey, Gauthier Gidel and Georgios Piliouras. COLT 2020. |
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14
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Lower Bounds and Conditioning of Differentiable Games By Adam Ibrahim, Waïss Azizian, Gauthier Gidel and Ioannis Mitliagkas. ICML 2020
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13
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A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games By Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien and Gauthier Gidel. AISTATS 2020.
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12
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A Closer Look at the Optimization Landscapes of Generative Adversarial Networks By Hugo Berard*, Gauthier Gidel*, Amjad Almahairi, Pascal Vincent and Simon Lacoste-Julien. * Equal contributions. ICLR 2020.
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11
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Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics By Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio and Guillaume Lajoie. NeurIPS 2019.
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10
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Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates By Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel and Simon Lacoste-Julien. NeurIPS 2019.
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9
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Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks By Gauthier Gidel, Francis Bach and Simon Lacoste-Julien. NeurIPS 2019. Accepted for a poster presentation to the ICML 2019 Workshop on Understanding and Improving Generalization in Deep Learning. |
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8
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Reducing Noise in GAN Training with Variance Reduced Extragradient
By Tatjana Chavdarova*, Gauthier Gidel*, François Fleuret and Simon Lacoste-Julien. Accepted for an oral presentation at the Generative Modeling and Model-Based Reasoning for Robotics and AI ICML 2019 Workshop. |
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7
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A Variational Inequality Perspective on Generative Adversarial Networks
Gauthier Gidel*, Hugo Berard*, Gaëtan Vignoud, Pascal Vincent and Simon Lacoste-Julien.
Paper
Webpage
Github
Poster
Slides
bibtex
Also accepted as an oral presentation at the Montreal AI Symposium 2018 @inproceedings{gidel2019variational, author = {Gidel, Gauthier and Berard, Hugo and Vincent, Pascal and Lacoste-Julien, Simon}, title = {A Variational Inequality Perspective on Generative Adversarial Nets}, booktitle = {ICLR}, year = {2019}, note = {(to appear)} } (TOP 15% of accepted submissions). |
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6
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Negative Momentum for Improved Game Dynamics
Gauthier Gidel*, Reyhane Askari Hemmat*, Mohammad Pezeshki, Gabriel Huang, Remi Lepriol, Simon Lacoste-Julien, Ioannis Mitliagkas.
Paper
bibtex
Accepted for a poster presentation at the Montreal AI Symposium 2018 and at the Modern Trends in Nonconvex Optimization for Machine Learning workshop (ICML 2018).
@article{gidel2019negative, author = {Gidel, Gauthier and Askari Hemmat, Reyhane and Pezeshki, Mohammad and Huang, Gabriel and Lepriol R\'emi and Lacoste-Julien, Simon and Mitliagkas, Ioannis}, title = {Negative Momentum for Improved Game Dynamics}, booktitle = {AISTATS}, year = {2019}, note = {(to appear)} } |
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5
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Adaptive Three Operator Splitting
Fabian Pedregosa, Gauthier Gidel.
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4
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Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa and Simon Lacoste-Julien.
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3
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Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
Gabriel Huang, Hugo Berard, Ahmed Touati, Gauthier Gidel, Pascal Vincent and Simon Lacoste-Julien.
@inproceedings{huang2017adversarial, author = {Huang, Gabriel and Gidel, Gauthier and Berard, Hugo and Touati Ahmed and Lacoste-Julien, Simon}, title = {Adversarial Divergences are Good Task Losses for Generative Modeling}, journal = {arXiv:1708.02511}, year = {2017} }Accepted as an oral presentation at the Montreal AI Symposium 2018 (TOP 15% of accepted submissions). Accepted as a contribution for the Principled Approaches in Deep Learning workshop (PADL), ICML2017. |
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2
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Frank-Wolfe Algorithms for Saddle Point Problems
Gauthier Gidel, Tony Jebara and Simon Lacoste-Julien.
Paper
bibtex
arXiv
Accepted as an oral presentation at the NeurIPS OPT2016 workshop @inproceedings{gidel2017saddle, author = {Gidel, Gauthier and Jebara, Tony and Lacoste-Julien, Simon}, title = {{F}rank-{W}olfe Algorithms for Saddle Point Problems}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)}, year = {2017} }HAL Code Project Poster (TOP 10% of accepted submissions). |
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1
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Extensions de l’algorithme de Frank-Wolfe pour la
recherche de points selles. (In French)
Gauthier Gidel.
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