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
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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

GilEstel Dizaji
PhD - Université de Montréal
alireza.dizaji@mila.quebec
Website
Github
Google Scholar
Beheshteh Toloueirakhshan
PhD - Université de Montréal
rakhshab@mila.quebec
Farzaneh Heidari
PhD - Université de Montréal
Co-supervisor :
Jian Tang
farzaneh.heidari@mila.quebec
Julia Gastinger
Collaborating Alumni - University of Mannheim
Co-supervisor :
Reihaneh Rabbany
julia.gastinger@mila.quebec
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Jun Dai
Postdoctorate - Université de Montréal
jun.dai@mila.quebec
Google Scholar
Marawan Gamal
PhD - Université de Montréal
marawan.gamal@mila.quebec
Github
Maude Lizaire
PhD - Université de Montréal
lizairem@mila.quebec
Michael Rizvi-Martel
Master's Research - Université de Montréal
michael.rizvi-martel@mila.quebec
Website
Github
Google Scholar
Omar Chikar
Master's Research - Université de Montréal
omar.chikhar@mila.quebec
Shirzadkhani Razieh Shirzadkhani
Collaborating researcher
Co-supervisor :
Reihaneh Rabbany
razieh.shirzadkhani@mila.quebec
Github
Shenyang Huang
PhD - McGill University
Principal supervisor :
Reihaneh Rabbany
huangshe@mila.quebec
Website
Github
Google Scholar
Soroush Omranpour
Master's Research - McGill University
Principal supervisor :
Reihaneh Rabbany
soroush.omranpour@mila.quebec
Github

Publications

Nonlinear Weighted Finite Automata
Tianyu Li
Guillaume Rabusseau
Doina Precup
Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent succ… (see more)esses of nonlinear models in machine learning, it is natural to wonder whether extending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinear WFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFA and relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.
2018-01-01
AISTATS (published)
proceedings.mlr.press
Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning
Di Wu
Guillaume Rabusseau
Vincent Francois-Lavet
Doina Precup
Benoit Boulet
Smart grids are advancing the management efficiency and security of power grids with the integration of energy storage, distributed controll… (see more)ers, and advanced meters. In particular, with the increasing prevalence of residential automation devices and distributed renewable energy generation, residential energy management is now drawing more attention. Meanwhile, the increasing adoption of electric vehicle (EV) brings more challenges and opportunities for smart residential energy management. This paper formalizes energy management for the residential home with EV charging as a Markov Decision Process and proposes reinforcement learning (RL) based control algorithms to address it. The objective of the proposed algorithms is to minimize the long-term operating cost. We further use a recurrent neural network (RNN) to model the electricity demand as a preprocessing step. Both the RNN prediction and latent representations are used as additional state features for the RL based control algorithms. Experiments on real-world data show that the proposed algorithms can significantly reduce the operating cost and peak power consumption compared to baseline control algorithms.
2018-01-01
(published)
www.semanticscholar.org
Tensor Regression Networks with various Low-Rank Tensor Approximations
Xingwei Cao
Guillaume Rabusseau
Joelle Pineau
Tensor regression networks achieve high compression rate of neural networks while having slight impact on performances. They do so by imposi… (see more)ng low tensor rank structure on the weight matrices of fully connected layers. In recent years, tensor regression networks have been investigated from the perspective of their compressive power, however, the regularization effect of enforcing low-rank tensor structure has not been investigated enough. We study tensor regression networks using various low-rank tensor approximations, aiming to compare the compressive and regularization power of different low-rank constraints. We evaluate the compressive and regularization performances of the proposed model with both deep and shallow convolutional neural networks. The outcome of our experiment suggests the superiority of Global Average Pooling Layer over Tensor Regression Layer when applied to deep convolutional neural network with CIFAR-10 dataset. On the contrary, shallow convolutional neural networks with tensor regression layer and dropout achieved lower test error than both Global Average Pooling and fully-connected layer with dropout function when trained with a small number of samples.
2017-12-27
ArXiv (preprint)
arxiv.org
arxiv.org
Neural Network Based Nonlinear Weighted Finite Automata
Tianyu Li
Guillaume Rabusseau
Doina Precup
Weighted finite automata (WFA) can expressively model functions defined over strings but are inherently linear models. Given the recent succ… (see more)esses of nonlinear models in machine learning, it is natural to wonder whether ex-tending WFA to the nonlinear setting would be beneficial. In this paper, we propose a novel model of neural network based nonlinearWFA model (NL-WFA) along with a learning algorithm. Our learning algorithm is inspired by the spectral learning algorithm for WFAand relies on a nonlinear decomposition of the so-called Hankel matrix, by means of an auto-encoder network. The expressive power of NL-WFA and the proposed learning algorithm are assessed on both synthetic and real-world data, showing that NL-WFA can lead to smaller model sizes and infer complex grammatical structures from data.
2017-09-13
ArXiv (preprint)
arxiv.org
arxiv.org
Hierarchical Methods of Moments
Matteo Ruffini
Guillaume Rabusseau
Borja Balle
Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal,… (see more) the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations. Our method is based on replacing the tensor decomposition step used in previous algorithms with approximate joint diagonalization. Experiments on topic modeling show that our method outperforms previous tensor decomposition methods in terms of speed and model quality.
arxiv.org
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.