Portrait de Guillaume Rabusseau

Guillaume Rabusseau

Membre académique principal
rabussgu@mila.quebec
Chaire en IA Canada-CIFAR
Professeur adjoint, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage profond
Apprentissage sur graphes
Factorisation tensorielle
Modèles probabilistes
Réseaux de neurones en graphes
Réseaux de neurones récurrents
Systèmes de recommandation
Théorie de l'apprentissage automatique
Théorie de l'information quantique
Site web
Google Scholar

Biographie

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

Publications

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset)
Dominique Beaini
Shenyang Huang
Joao Alex Cunha
Zhiyi Li
Gabriela Moisescu-Pareja
Oleksandr Dymov
Samuel Maddrell-Mander
Callum McLean
Ali Parviz
Luis T. Díaz Müller
Jama Hussein Mohamud
Frederik Wenkel
Michael Craig
Michał Koziarski
Jiarui Lu
Zhaocheng Zhu
Cristian Gabellini
Guillaume Rabusseau
Reihaneh Rabbany
Jian Tang … (voir 8 de plus)
Christopher G. Morris
Mirco Ravanelli
Guy Wolf
Prudencio Tossou
Hadrien Mary
Błażej Banaszewski
Chad Martin
Dominic Masters
2023-09-21
Zenodo (inconnu)
doi.org
Preface
François Coste
Faissal Ouardi
Guillaume Rabusseau
2023-07-04
Proceedings of 16th edition of the International Conference on Grammatical Inference (publié)
proceedings.mlr.press
proceedings.mlr.press
ROSA: Random Orthogonal Subspace Adaptation
Marawan Gamal
Guillaume Rabusseau
Aristides Milios
Siva Reddy
2023-06-19
ICML.cc/2023/Workshop/ES-FoMO (poster)
openreview.net
openreview.net
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
Shenyang Huang
Jacob Danovitch
Guillaume Rabusseau
Reihaneh Rabbany
2023-05-14
arXiv (prépublication)
doi.org
arxiv.org
Benchmarking State-Merging Algorithms for Learning Regular Languages.
Adil Soubki
Jeffrey Heinz
François Coste
Faissal Ouardi
Guillaume Rabusseau
2022-12-31
International Conference on Graphics and Interaction (publié)
dblp.uni-trier.de
Explaining Graph Neural Networks Using Interpretable Local Surrogates
Farzaneh Heidari
Perouz Taslakian
Guillaume Rabusseau
2022-12-31
TAG-ML (publié)
proceedings.mlr.press
proceedings.mlr.press
Formal and Empirical Studies of Counting Behaviour in ReLU RNNs.
Nadine El-Naggar
Andrew Ryzhikov
Laure Daviaud
Pranava Madhyastha
Tillman Weyde
François Coste
Faissal Ouardi
Guillaume Rabusseau
2022-12-31
International Conference on Graphics and Interaction (publié)
dblp.uni-trier.de
Identification of Substitutable Context-Free Languages over Infinite Alphabets from Positive Data
Yutaro Numaya
Diptarama Hendrian
Ryo Yoshinaka
Ayumi Shinohara
François Coste
Faissal Ouardi
Guillaume Rabusseau
This paper is concerned with the identification in the limit from positive data of sub-stitutable context-free languages cfl s) over infinit… (voir plus)e alphabets. Clark and Eyraud (2007) showed that substitutable cfl s over finite alphabets are learnable in this learning paradigm. We show that substitutable cfl s generated by grammars whose production rules may have predicates that represent sets of potentially infinitely many terminal symbols in a compact manner are learnable if the terminal symbol sets represented by those predicates are learnable, under a certain condition. This can be seen as a result parallel to Argyros and D’Antoni’s work (2018) that amplifies the query learnability of predicate classes to that of symbolic automata classes. Our result is the first that shows such amplification is possible for identifying some cfl s in the limit from positive data.
2022-12-31
ICGI (publié)
dblp.uni-trier.de
Learning Syntactic Monoids from Samples by extending known Algorithms for learning State Machines
Simon Dieck
Sicco Verwer
François Coste
Faissal Ouardi
Guillaume Rabusseau
For the inference of regular languages, most current methods learn a version of deterministic finite automata. Syntactic monoids are an alte… (voir plus)rnative representation of regular languages, which have some advantages over automata. For example, traces can be parsed starting from any index and the star-freeness of the language they represent can be checked in polynomial time. But, to date, there existed no passive learning algorithm for syntactic monoids. In this paper, we prove that known state-merging algorithms for learning deterministic finite automata can be instrumented to learn syntactic monoids instead, by using as the input a special structure proposed in this paper: the interfix-graph. Further, we introduce a method to encode frequencies on the interfix-graph, such that models can also be learned from only positive traces. We implemented this structure and performed experiments with both traditional data and data containing only positive traces. As such this work answers basic theoretical and experimental questions regarding a novel passive learning algorithm for syntactic monoids.
2022-12-31
International Conference on Graphics and Interaction (publié)
dblp.uni-trier.de
Lower Bounds for Active Automata Learning.
Loes Kruger
Bharat Garhewal
François Coste
Frits W. Vaandrager
Faissal Ouardi
Guillaume Rabusseau
2022-12-31
ICGI (publié)
dblp.uni-trier.de
Spectral Regularization: an Inductive Bias for Sequence Modeling
Kaiwen Hou
Guillaume Rabusseau
Hou Rabusseau
2022-11-03
ArXiv (prépublication)
doi.org
arxiv.org
Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata
Tianyu Li
Bogdan Mazoure
Guillaume Rabusseau
Weighted finite automata (WFAs) have been widely applied in many fields. One of the classic problems for WFAs is probability distribution es… (voir plus)timation over sequences of discrete symbols. Although WFAs have been extended to deal with continuous input data, namely continuous WFAs (CWFAs), it is still unclear how to approximate density functions over sequences of continuous random variables using WFA-based models, due to the limitation on the expressiveness of the model as well as the tractability of approximating density functions via CWFAs. In this paper, we propose a nonlinear extension to the CWFA model to first improve its expressiveness, we refer to it as the nonlinear continuous WFAs (NCWFAs). Then we leverage the so-called RNADE method, which is a well-known density estimator based on neural networks, and propose the RNADE-NCWFA model. The RNADE-NCWFA model computes a density function by design. We show that this model is strictly more expressive than the Gaussian HMM model, which CWFA cannot approximate. Empirically, we conduct a synthetic experiment using Gaussian HMM generated data. We focus on evaluating the model's ability to estimate densities for sequences of varying lengths (longer length than the training data). We observe that our model performs the best among the compared baseline methods.
2022-06-07
arXiv (prépublication)
doi.org
arxiv.org