Gauthier Gidel

sadhana.anand@mila.quebec

Biographie

Je suis professeur adjoint au Département d’informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal et membre académique principal de Mila – Institut québécois d’intelligence artificielle. J'ai obtenu une bourse Borealis AI destinée aux étudiant·e·s des cycles supérieurs et je suis actuellement titulaire d'une chaire en IA Canada-CIFAR. J'ai travaillé chez DeepMind et Element AI, et j'ai récemment été un visiteur de longue durée au Simons Institute de l’Université de Californie à Berkeley. Mes intérêts de recherche se situent à l'intersection de la théorie des jeux, de l'optimisation et de l'apprentissage automatique.

Étudiants actuels

Sadhana Anand

Maîtrise recherche - UdeM

yuan-chen.chang@mila.quebec

Yuan-Chen Chang

Stagiaire de recherche - UdeM

David Dobre

Doctorat - UdeM

david-a.dobre@mila.quebec

damien.ferbach@mila.quebec

Nouha Dziri

Visiteur de recherche indépendant - N/A

nouha.dziri@gmail.com

Doctorat - UdeM

Co-superviseur⋅e :

Courtney Paquette

niklas.herbster@gmail.com

Niklas Herbster

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Irina Rish

marco.jiralerspong@mila.quebec

Marco Jiralerspong

Doctorat - UdeM

Zichu Liu

Doctorat - UdeM

Co-superviseur⋅e :

Ioannis Mitliagkas

zichu.liu@mila.quebec

Orel Mazor

Collaborateur·rice de recherche - ENS Lyon

orel.mazor@mila.quebec

andjela.mladenovic@mila.quebec

Andjela Mladenovic

Doctorat - UdeM

Co-superviseur⋅e :

Aaron Courville

mehrnaz.mofakhami@mila.quebec

Mehrnaz Mofakhami

Collaborateur·rice de recherche - UdeM

Co-superviseur⋅e :

Ioannis Mitliagkas

Collaborateur·rice de recherche - UdeM

ailin.rasteh@mila.quebec

Leo Schwinn

Visiteur de recherche indépendant - Technical Univeristy of Munich

leo.schwinn@mila.quebec

Tom Stanic

Stagiaire de recherche - UdeM

tom.stanic@mila.quebec

tommaso.tosato@mila.quebec

Tommaso Tosato

Postdoctorat - UdeM

Doctorat - UdeM

danilo.vucetic@mila.quebec

bora.yongacoglu@mila.quebec

Sophie Xhonneux

Doctorat - UdeM

Co-superviseur⋅e :

Collaborateur·rice alumni - N/A

Billets de blogue

13 juin 2024

Que nous apprennent les distributions des coefficients synaptiques au sujet de l’apprentissage dans le cerveau ?

par

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Lire l'article

Publications

Expected Flow Networks in Stochastic Environments and Two-Player Zero-Sum Games

Bilun Sun

Nikolay Malkin

Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully … (voir plus)applied to various structured object generation tasks, sampling a diverse set of high-reward objects quickly. We propose expected flow networks (EFlowNets), which extend GFlowNets to stochastic environments. We show that EFlowNets outperform other GFlowNet formulations in stochastic tasks such as protein design. We then extend the concept of EFlowNets to adversarial environments, proposing adversarial flow networks (AFlowNets) for two-player zero-sum games. We show that AFlowNets learn to find above 80% of optimal moves in Connect-4 via self-play and outperform AlphaZero in tournaments.

2024-01-15

ICLR.cc/2024/Conference (poster)

Synaptic Weight Distributions Depend on the Geometry of Plasticity

Blake Aaron Richards

A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic … (voir plus)plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the choice of distance for synaptic changes - i.e. the geometry of synaptic plasticity. Gradient descent assumes that the distance is Euclidean, but many other distances are possible, and there is no reason that biology necessarily uses Euclidean geometry. Here, using the theoretical tools provided by mirror descent, we show that the distribution of synaptic weights will depend on the geometry of synaptic plasticity. We use these results to show that experimentally-observed log-normal weight distributions found in several brain areas are not consistent with standard gradient descent (i.e. a Euclidean geometry), but rather with non-Euclidean distances. Finally, we show that it should be possible to experimentally test for different synaptic geometries by comparing synaptic weight distributions before and after learning. Overall, our work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.

2024-01-15

ICLR.cc/2024/Conference (spotlight)

On the Stability of Iterative Retraining of Generative Models on Their Own Data

Avishek (Joey) Bose

Deep generative models have made tremendous progress in modeling complex data, often exhibiting generation quality that surpasses a typical … (voir plus)human's ability to discern the authenticity of samples. Undeniably, a key driver of this success is enabled by the massive amounts of web-scale data consumed by these models. Due to these models' striking performance and ease of availability, the web will inevitably be increasingly populated with synthetic content. Such a fact directly implies that future iterations of generative models will be trained on both clean and artificially generated data from past models. In this paper, we develop a framework to rigorously study the impact of training generative models on mixed datasets -- from classical training on real data to self-consuming generative models trained on purely synthetic data. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough and the proportion of clean training data (w.r.t. synthetic data) is large enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models on CIFAR10 and FFHQ.

2024-01-15

ICLR.cc/2024/Conference (spotlight)

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.

Eduard Gorbunov

Abdurakhmon Sadiev

Marina Danilova

Samuel Horváth

Pavel Dvurechensky

Alexander Gasnikov

Peter Richtárik

2023-12-31

International Conference on Machine Learning (publié)

Proving Linear Mode Connectivity of Neural Networks via Optimal Transport

Damien Ferbach

Baptiste Goujaud

Aymeric Dieuleveut

The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neura… (voir plus)l network architectures. Recent works have experimentally shown that two different solutions found after two runs of a stochastic training are often connected by very simple continuous paths (e.g., linear) modulo a permutation of the weights. In this paper, we provide a framework theoretically explaining this empirical observation. Based on convergence rates in Wasserstein distance of empirical measures, we show that, with high probability, two wide enough two-layer neural networks trained with stochastic gradient descent are linearly connected. Additionally, we express upper and lower bounds on the width of each layer of two deep neural networks with independent neuron weights to be linearly connected. Finally, we empirically demonstrate the validity of our approach by showing how the dimension of the support of the weight distribution of neurons, which dictates Wasserstein convergence rates is correlated with linear mode connectivity.

2023-12-31

AISTATS (publié)

Adversarial Attacks and Defenses in Large Language Models: Old and New Threats

Leo Schwinn

David Dobre

Stephan Günnemann

Over the past decade, there has been extensive research aimed at enhancing the robustness of neural networks, yet this problem remains vastl… (voir plus)y unsolved. Here, one major impediment has been the overestimation of the robustness of new defense approaches due to faulty defense evaluations. Flawed robustness evaluations necessitate rectifications in subsequent works, dangerously slowing down the research and providing a false sense of security. In this context, we will face substantial challenges associated with an impending adversarial arms race in natural language processing, specifically with closed-source Large Language Models (LLMs), such as ChatGPT, Google Bard, or Anthropic's Claude. We provide a first set of prerequisites to improve the robustness assessment of new approaches and reduce the amount of faulty evaluations. Additionally, we identify embedding space attacks on LLMs as another viable threat model for the purposes of generating malicious content in open-sourced models. Finally, we demonstrate on a recently proposed defense that, without LLM-specific best practices in place, it is easy to overestimate the robustness of a new approach.

2023-10-26

NeurIPS.cc/2023/Workshop/ICBINB (publié)

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure

Angela Yuan

Chris Junchi Li

Michael Jordan

Quanquan Gu

Simon Shaolei Du

We consider the problem of solving stochastic monotone variational inequalities with a separable structure using a stochastic first-order or… (voir plus)acle. Building on standard extragradient for variational inequalities we propose a novel algorithm---stochastic \emph{accelerated gradient-extragradient} (AG-EG)---for strongly monotone variational inequalities (VIs). Our approach combines the strengths of extragradient and Nesterov acceleration. By showing that its iterates remain in a bounded domain and applying scheduled restarting, we prove that AG-EG has an optimal convergence rate for strongly monotone VIs. Furthermore, when specializing to the particular case of bilinearly coupled strongly-convex-strongly-concave saddle-point problems, including bilinear games, our algorithm achieves fine-grained convergence rates that match the respective lower bounds, with the stochasticity being characterized by an additive statistical error term that is optimal up to a constant prefactor.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

AI4GCC - Track 3: Consumption and the Challenges of Multi-Agent RL

Marco Jiralerspong

2023-08-08

ArXiv (prépublication)

arxiv.org

Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity

Eduard Gorbunov

Adrien Taylor

Samuel Horváth

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance

Abdurakhmon Sadiev

Marina Danilova

Eduard Gorbunov

Samuel Horváth

Pavel Dvurechensky

Alexander Gasnikov

Peter Richtárik

During the recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimiza… (voir plus)tion methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as boundedness of the gradient noise variance or of the objective’s gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded central

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

Omega: Optimistic EMA Gradients

Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training… (voir plus). Although game optimization is fairly well understood in the deterministic setting, some issues persist in the stochastic regime. Recent work has shown that stochastic gradient descent-ascent methods such as the optimistic gradient are highly sensitive to noise or can fail to converge. Although alternative strategies exist, they can be prohibitively expensive. We introduce Omega, a method with optimistic-like updates that mitigates the impact of noise by incorporating an EMA of historic gradients in its update rule. We also explore a variation of this algorithm that incorporates momentum. Although we do not provide convergence guarantees, our experiments on stochastic games show that Omega outperforms the optimistic gradient method when applied to linear players.

2023-07-01

ICML.cc/2023/Workshop/LXAI_Regular_Deadline (présentation orale)

Raising the Bar for Certified Adversarial Robustness with Diffusion Models

Thomas Altstidl

David Dobre

Bjoern Eskofier

Leo Schwinn

Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirica… (voir plus)l methods such as adversarial training, whose effectiveness is often later reduced by unseen attacks. Still, the limited certified robustness that is currently achievable has been a bottleneck for their practical adoption. Gowal et al. and Wang et al. have shown that generating additional training data using state-of-the-art diffusion models can considerably improve the robustness of adversarial training. In this work, we demonstrate that a similar approach can substantially improve deterministic certified defenses. In addition, we provide a list of recommendations to scale the robustness of certified training approaches. One of our main insights is that the generalization gap, i.e., the difference between the training and test accuracy of the original model, is a good predictor of the magnitude of the robustness improvement when using additional generated data. Our approach achieves state-of-the-art deterministic robustness certificates on CIFAR-10 for the

2023-05-16

arXiv (prépublication)