Portrait of Guillaume Rabusseau

Guillaume Rabusseau

Core Academic Member
rabussgu@mila.quebec
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Deep Learning
Graph Neural Networks
Learning on Graphs
Machine Learning Theory
Probabilistic Models
Quantum Information Theory
Recommender Systems
Recurrent Neural Networks
Tensor Factorization
Website
Google Scholar

Biography

I have been an assistant professor at Mila – Quebec Artificial Intelligence Institute and in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal (UdeM) since September 2018. I was awarded a Canada CIFAR AI Chair in March 2019. Before joining UdeM, I was a postdoctoral research fellow in the Reasoning and Learning Lab at McGill University, where I worked with Prakash Panangaden, Joelle Pineau and Doina Precup.

I obtained my PhD in 2016 from Aix-Marseille University (AMU) in France, where I worked in the Qarma team (Machine Learning and Multimedia) under the supervision of François Denis and Hachem Kadri. I also obtained my MSc in fundamental computer science and my BSc in computer science from AMU. I am interested in tensor methods for machine learning and in designing learning algorithms for structured data by leveraging linear and multilinear algebra (e.g., spectral methods).

Current Students

Omar Chikar
Master's Research - Université de Montréal
Jun Dai
Postdoctorate - Université de Montréal
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Alireza Dizaji
PhD - Université de Montréal
Github
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Marawan Gamal
PhD - Université de Montréal
Github
Julia Gastinger
Collaborating researcher - University of Mannheim
Co-supervisor :
Reihaneh Rabbany
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Farzaneh Heidari
PhD - Université de Montréal
Co-supervisor :
Jian Tang
Shenyang Huang
PhD - McGill University
Principal supervisor :
Reihaneh Rabbany
Website
Github
Google Scholar
Maude Lizaire
PhD - Université de Montréal
Google Scholar
Soroush Omranpour
Master's Research - McGill University
Principal supervisor :
Reihaneh Rabbany
Github
Razieh Razieh Shirzadkhani
Collaborating researcher
Co-supervisor :
Reihaneh Rabbany
Github
Michael Rizvi-Martel
PhD - Université de Montréal
Co-supervisor :
Adam M. Oberman
Website
Github
Google Scholar
Pascal Tikeng Notsawo
PhD - Université de Montréal
Co-supervisor :
Guillaume Dumas
Website
Github
Google Scholar
Beheshteh Toloueirakhshan
PhD - Université de Montréal

Publications

Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau
Borja Balle
Joelle Pineau
We consider the problem of estimating multiple related functions computed by weighted automata~(WFA). We first present a natural notion of r… (see more)elatedness between WFAs by considering to which extent several WFAs can share a common underlying representation. We then introduce the model of vector-valued WFA which conveniently helps us formalize this notion of relatedness. Finally, we propose a spectral learning algorithm for vector-valued WFAs to tackle the multitask learning problem. By jointly learning multiple tasks in the form of a vector-valued WFA, our algorithm enforces the discovery of a representation space shared between tasks. The benefits of the proposed multitask approach are theoretically motivated and showcased through experiments on both synthetic and real world datasets.