Gauthier Gidel

sadhana.anand@mila.quebec

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

Sadhana Anand

Master's Research - Université de Montréal

yuan-chen.chang@mila.quebec

Yuan-Chen Chang

PhD - Université de Montréal

David Dobre

PhD - Université de Montréal

david-a.dobre@mila.quebec

damien.ferbach@mila.quebec

Nouha Dziri

Independent visiting researcher - N/A

nouha.dziri@gmail.com

PhD - Université de Montréal

Co-supervisor :

Courtney Paquette

niklas.herbster@gmail.com

Niklas Herbster

Research Intern - Université de Montréal

Principal supervisor :

Irina Rish

marco.jiralerspong@mila.quebec

Marco Jiralerspong

PhD - Université de Montréal

andjela.mladenovic@mila.quebec

Zichu Liu

PhD - Université de Montréal

Co-supervisor :

Ioannis Mitliagkas

zichu.liu@mila.quebec

Andjela Mladenovic

PhD - Université de Montréal

Co-supervisor :

Aaron Courville

mehrnaz.mofakhami@mila.quebec

Mehrnaz Mofakhami

Collaborating researcher - Université de Montréal

Co-supervisor :

Ioannis Mitliagkas

Collaborating researcher - Université de Montréal

ailin.rasteh@mila.quebec

Leo Schwinn

Independent visiting researcher - Technical Univeristy of Munich

leo.schwinn@mila.quebec

Tom Stanic

Master's Research - Université de Montréal

tom.stanic@mila.quebec

tommaso.tosato@mila.quebec

Tommaso Tosato

Postdoctorate - Université de Montréal

PhD - Université de Montréal

danilo.vucetic@mila.quebec

Sophie Xhonneux

PhD - Université de Montréal

Co-supervisor :

Jian Tang

lpxhonneux@gmail.com

What Do Synaptic Weight Distributions Tell Us About Learning in the Brain ?

Blog Posts

June 13, 2024

Roman Pogodin

Jonathan Cornford

Arna Ghosh

Gauthier Gidel

Guillaume Lajoie

Blake Richards

Read the article

Publications

Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space

Stephan Günnemann

Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be direc… (see more)tly transferred to closed-source models. However, this approach neglects the steady progression of open-source models. As open-source models advance in capability, ensuring their safety also becomes increasingly imperative. Yet, attacks tailored to open-source LLMs that exploit full model access remain largely unexplored. We address this research gap and propose the embedding space attack, which directly attacks the continuous embedding representation of input tokens. We find that embedding space attacks circumvent model alignments and trigger harmful behaviors more efficiently than discrete attacks or model fine-tuning. Furthermore, we present a novel threat model in the context of unlearning and show that embedding space attacks can extract supposedly deleted information from unlearned LLMs across multiple datasets and models. Our findings highlight embedding space attacks as an important threat model in open-source LLMs. Trigger Warning: the appendix contains LLM-generated text with violence and harassment.

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

On the Scalability of Certified Adversarial Robustness with Generated Data

Thomas Altstidl

David Dobre

Arthur Kosmala

Björn Eskofier

Leo Schwinn

Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirica… (see more)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 but also reveal notable differences in the scaling behavior between certified and empirical methods. In addition, we provide a list of recommendations to scale the robustness of certified training approaches. Our approach achieves state-of-the-art deterministic robustness certificates on CIFAR-10 for the ℓ 2 ( ϵ = 36 / 255 ) and ℓ ∞ ( ϵ = 8 / 255 ) threat models, outperforming the previous results by +3 . 95 and +1 . 39 percentage points, respectively. Furthermore, we report similar improvements for CIFAR-100.

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

General Causal Imputation via Synthetic Interventions

Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their inte… (see more)ractions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.

2024-10-29

NeurIPS.cc/2024/Workshop/CRL (poster)

Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation

Michael Przystupa

Matthew E. Taylor

Martin Jagersand

Justus Piater

Samuele Tosatto

As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay peop… (see more)le be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use state-conditioned action mapping methods to learn mappings between low-dimensional controllers and high DOF manipulators -- prior research suggests these mappings can simplify the teleoperation experience for users. Recent works suggest that neural networks predicting a local linear function are superior to the typical end-to-end multi-layer perceptrons because they allow users to more easily undo actions, providing more control over the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced actions in training data. We observe that the benefit of these mappings is being an odd function concerning user actions, and propose end-to-end nonlinear action maps which achieve this property. Unfortunately, our experiments show that such modifications offer minimal advantages over previous solutions. We find that nonlinear odd functions behave linearly for most of the control space, suggesting architecture structure improvements are not the primary factor in data-driven teleoperation. Our results suggest other avenues, such as data augmentation techniques and analysis of human behavior, are necessary for action maps to become practical in real-world applications, such as in assistive robotics to improve the quality of life of people living with w disability.

2024-10-27

ArXiv (preprint)

arxiv.org

Local Linearity is All You Need (in Data-Driven Teleoperation)

Michael Przystupa

Matthew E. Taylor

Martin Jagersand

Justus Piater

Samuele Tosatto

One of the critical aspects of assistive robotics is to provide a control system of a high-dimensional robot from a low-dimensional user inp… (see more)ut (i.e. a 2D joystick). Data-driven teleoperation seeks to provide an intuitive user interface called an action map to map the low dimensional input to robot velocities from human demonstrations. Action maps are machine learning models trained on robotic demonstration data to map user input directly to desired movements as opposed to aspects of robot pose ("move to cup or pour content" vs. "move along x- or y-axis"). Many works have investigated nonlinear action maps with multi-layer perceptrons, but recent work suggests that local-linear neural approximations provide better control of the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced motions in training data. In this work, we hypothesize that local-linear neural networks are effective because they make the action map odd w.r.t. the user input, enhancing the intuitiveness of the controller. Based on this assumption, we propose two nonlinear means of encoding odd behavior that do not constrain the action map to a local linear function. However, our analysis reveals that these models effectively behave like local linear models for relevant mappings between user joysticks and robot movements. We support this claim in simulation, and show on a realworld use case that there is no statistical benefit of using non-linear maps, according to the users experience. These negative results suggest that further investigation into model architectures beyond local linear models may offer diminishing returns for improving user experience in data-driven teleoperation systems.

2024-10-13

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Avishek Joey Bose

Cheng-Hao Liu

Nikolay Malkin

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (see more)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient -- and no data samples -- to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-07-22

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

A Persuasive Approach to Combating Misinformation

Safwan Hossain

Andjela Mladenovic

Yiling Chen

Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine… (see more) learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user's future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting and discuss the broader scope of using information design to combat misinformation.

2024-07-22

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

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

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

In-Context Learning, Can It Break Safety?

2024-06-27

ICML.cc/2024/Workshop/NextGenAISafety (poster)

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 … (see more)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.

2024-05-06

International Conference on Learning Representations (Accept (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 … (see more)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.

2024-05-06

International Conference on Learning Representations (Accept (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 … (see more)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.

2024-05-06

International Conference on Learning Representations (Accept (spotlight))