Portrait de Guillaume Rabusseau

Guillaume Rabusseau

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur adjoint, Université de Montréal, Département d'informatique et de recherche opérationnelle


Depuis septembre 2018, je suis professeur adjoint à Mila – Institut québécois d’intelligence artificielle et au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal (UdeM). Je suis titulaire d’une chaire de recherche en IA Canada-CIFAR depuis mars 2019. Avant de me joindre à l’UdeM, j’ai été chercheur postdoctoral au laboratoire de raisonnement et d'apprentissage de l'Université McGill, où j'ai travaillé avec Prakash Panangaden, Joelle Pineau et Doina Precup.

J'ai obtenu mon doctorat en 2016 à l’Université d’Aix-Marseille (AMU), où j'ai travaillé dans l'équipe Qarma (apprentissage automatique et multimédia), sous la supervision de François Denis et Hachem Kadri. Auparavant, j'ai obtenu une maîtrise en informatique fondamentale de l'AMU et une licence en informatique de la même université en formation à distance.

Je m'intéresse aux méthodes de tenseurs pour l'apprentissage automatique et à la conception d'algorithmes d'apprentissage pour les données structurées par l’utilisation de l'algèbre linéaire et multilinéaire (par exemple, les méthodes spectrales).

Étudiants actuels

Doctorat - Université de Montréal
Doctorat - Université de Montréal
Co-superviseur⋅e :
Collaborateur·rice alumni - University of Mannheim
Co-superviseur⋅e :
Postdoctorat - Université de Montréal
Doctorat - Université de Montréal
Doctorat - Université de Montréal
Maîtrise recherche - Université de Montréal
Collaborateur·rice de recherche
Co-superviseur⋅e :
Doctorat - McGill University
Superviseur⋅e principal⋅e :
Maîtrise recherche - McGill University
Superviseur⋅e principal⋅e :


Simulating weighted automata over sequences and trees with transformers
Michael Rizvi
Maude Lizaire
Clara Lacroce
Simulating Weighted Automata over Sequences and Trees with Transformers
Michael Rizvi
Maude Lizaire
Clara Lacroce
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Shenyang Huang
Joao Alex Cunha
Zhiyi Li
Gabriela Moisescu-Pareja
Oleksandr Dymov
Samuel Maddrell-Mander
Callum McLean
Frederik Wenkel
Luis Müller
Jama Hussein Mohamud
Ali Parviz
Michael Craig
Michał Koziarski
Jiarui Lu
Zhaocheng Zhu
Cristian Gabellini
Kerstin Klaser
Josef Dean
Cas Wognum … (voir 15 de plus)
Maciej Sypetkowski
Christopher Morris
Ioannis Koutis
Prudencio Tossou
Hadrien Mary
Therence Bois
Andrew William Fitzgibbon
Blazej Banaszewski
Chad Martin
Dominic Masters
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, wh… (voir plus)ere datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks. The Graphium library is publicly available on Github and the dataset links are available in Part 1 and Part 2.
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Shenyang Huang
Samy Coulombe
Yasmeen Hitti
Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly dete… (voir plus)ction in temporal graphs is crucial for many real-world applications such as intrusion identification in network systems, detection of ecosystem disturbances, and detection of epidemic outbreaks. In this article, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: (i) how to compare graph snapshots across time, (ii) how to capture temporal dependencies, and (iii) how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short-term and long-term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that (i) LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, (ii) MultiLAD’s advantage over contenders significantly increases when additional views are available, and (iii) MultiLAD is highly robust to noise from individual views. In five real-world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
Generative Learning of Continuous Data by Tensor Networks
Alex Meiburg
Jing Chen
Jacob Miller
Raphaelle Tihon
Alejandro Perdomo-ortiz
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Shenyang Huang
Farimah Poursafaei
Jacob Danovitch
Matthias Fey
Weihua Hu
Emanuele Rossi
Jure Leskovec
Michael M. Bronstein
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and r… (voir plus)obust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/.
ROSA: Random Orthogonal Subspace Adaptation
Marawan Gamal
Aristides Milios
Optimal Approximate Minimization of One-Letter Weighted Finite Automata
Clara Lacroce
Borja Balle
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
Shenyang Huang
Jacob Danovitch
Recurrent Real-valued Neural Autoregressive Density Estimator for Online Density Estimation and Classification of Streaming Data
Tianyu Li
Bogdan Mazoure
In contrast with the traditional offline learning, where complete data accessibility is assumed, many modern applications involve processing… (voir plus) data in a streaming fashion. This online learning setting raises various challenges, including concept drift, hardware memory constraints, etc. In this paper, we propose the Recurrent Real-valued Neural Autoregressive Density Estimator (RRNADE), a flexible density-based model for online classification and density estimation. RRNADE combines a neural Gaussian mixture density module with a recurrent module. This combination allows RRNADE to exploit possible sequential correlations in the streaming task, which are often ignored in the classical streaming setting where each input is assumed to be independent from the previous ones. We showcase the ability of RRNADE to adapt to concept drifts on synthetic density estimation tasks. We also apply RRNADE to online classification tasks on both real world and synthetic datasets and compare it with multiple density based as well as nondensity based online classification methods. In almost all of these tasks, RRNADE outperforms the other methods. Lastly, we conduct an ablation study demonstrating the complementary benefits of the density and the recurrent modules.
Benchmarking State-Merging Algorithms for Learning Regular Languages.
Adil Soubki
Jeffrey Heinz
François Coste
Faissal Ouardi