Gauthier Gidel

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Université de Montréal

Adversarial Example Games

The existence of adversarial examples capable of fooling trained neural ...
07/01/2020 ∙ by Avishek Joey Bose, et al. ∙ 13 ∙ share

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Real World Games Look Like Spinning Tops

This paper investigates the geometrical properties of real world games (...
04/20/2020 ∙ by Wojciech Marian Czarnecki, et al. ∙ 10 ∙ share

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Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets

Adversarial training, a special case of multi-objective optimization, is...
02/14/2020 ∙ by Gauthier Gidel, et al. ∙ 0 ∙ share

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Accelerating Smooth Games by Manipulating Spectral Shapes

We use matrix iteration theory to characterize acceleration in smooth ga...
01/02/2020 ∙ by Waïss Azizian, et al. ∙ 16 ∙ share

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Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent

Gradient descent is arguably one of the most popular online optimization...
07/09/2019 ∙ by James P. Bailey, et al. ∙ 0 ∙ share

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Lower Bounds and Conditioning of Differentiable Games

Many recent machine learning tools rely on differentiable game formulati...
06/17/2019 ∙ by Adam Ibrahim, et al. ∙ 0 ∙ share

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A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games

We consider differentiable games: multi-objective minimization problems,...
06/13/2019 ∙ by Waïss Azizian, et al. ∙ 0 ∙ share

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A Closer Look at the Optimization Landscapes of Generative Adversarial Networks

Generative adversarial networks have been very successful in generative ...
06/11/2019 ∙ by Hugo Berard, et al. ∙ 1 ∙ share

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Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

A recent strategy to circumvent the exploding and vanishing gradient pro...
05/28/2019 ∙ by Giancarlo Kerg, et al. ∙ 1 ∙ share

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Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

Recent works have shown that stochastic gradient descent (SGD) achieves ...
05/24/2019 ∙ by Sharan Vaswani, et al. ∙ 10 ∙ share

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Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks

When optimizing over-parameterized models, such as deep neural networks,...
04/30/2019 ∙ by Gauthier Gidel, et al. ∙ 2 ∙ share

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Reducing Noise in GAN Training with Variance Reduced Extragradient

Using large mini-batches when training generative adversarial networks (...
04/18/2019 ∙ by Tatjana Chavdarova, et al. ∙ 38 ∙ share

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Negative Momentum for Improved Game Dynamics

Games generalize the optimization paradigm by introducing different obje...
07/12/2018 ∙ by Gauthier Gidel, et al. ∙ 10 ∙ share

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Frank-Wolfe Splitting via Augmented Lagrangian Method

Minimizing a function over an intersection of convex sets is an importan...
04/09/2018 ∙ by Gauthier Gidel, et al. ∙ 0 ∙ share

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Adaptive Three Operator Splitting

We propose and analyze a novel adaptive step size variant of the Davis-Y...
04/06/2018 ∙ by Fabian Pedregosa, et al. ∙ 0 ∙ share

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A Variational Inequality Perspective on Generative Adversarial Nets

Stability has been a recurrent issue in training generative adversarial ...
02/28/2018 ∙ by Gauthier Gidel, et al. ∙ 0 ∙ share

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Parametric Adversarial Divergences are Good Task Losses for Generative Modeling

Generative modeling of high dimensional data like images is a notoriousl...
08/08/2017 ∙ by Gabriel Huang, et al. ∙ 0 ∙ share

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Frank-Wolfe Algorithms for Saddle Point Problems

We extend the Frank-Wolfe (FW) optimization algorithm to solve constrain...
10/25/2016 ∙ by Gauthier Gidel, et al. ∙ 0 ∙ share

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Gauthier Gidel

Featured Co-authors

Yoshua Bengio

Francis Bach

Simon Lacoste-Julien

Pascal Vincent

Mark Schmidt

Karl Tuyls

David Balduzzi

Georgios Piliouras

François Fleuret

William L. Hamilton

Ioannis Mitliagkas

Adversarial Example Games

Real World Games Look Like Spinning Tops

Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets

Accelerating Smooth Games by Manipulating Spectral Shapes

Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent

Lower Bounds and Conditioning of Differentiable Games

A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games

A Closer Look at the Optimization Landscapes of Generative Adversarial Networks

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks

Reducing Noise in GAN Training with Variance Reduced Extragradient

Negative Momentum for Improved Game Dynamics

Frank-Wolfe Splitting via Augmented Lagrangian Method

Adaptive Three Operator Splitting

A Variational Inequality Perspective on Generative Adversarial Nets

Parametric Adversarial Divergences are Good Task Losses for Generative Modeling

Frank-Wolfe Algorithms for Saddle Point Problems