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Guillaume Rabusseau
Guillaume Rabusseau
Assistant Professor - Canada CIFAR AI Chair, Université de Montréal / Mila
Verified email at iro.umontreal.ca - Homepage
Title
Cited by
Cited by
Year
Low-Rank Regression with Tensor Responses
G Rabusseau, H Kadri
Advances in Neural Information Processing Systems, 1867-1875, 2016
602016
Connecting weighted automata and recurrent neural networks through spectral learning
G Rabusseau, T Li, D Precup
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
302019
Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin
V Makarenkov, B Mazoure, G Rabusseau, P Legendre
BMC ecology and evolution 21 (1), 1-18, 2021
292021
Tensor networks for probabilistic sequence modeling
J Miller, G Rabusseau, J Terilla
International Conference on Artificial Intelligence and Statistics, 3079-3087, 2021
20*2021
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
V François-Lavet, G Rabusseau, J Pineau, D Ernst, R Fonteneau
Journal of Artificial Intelligence Research 65, 1-30, 2019
202019
Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning
RG Di Wu, F lavet Vincent, P Doina, B Benoit
Proceedings of the 16th Adaptive Learning Agents, 2018
192018
Tensor regression networks with various low-rank tensor approximations
X Cao, G Rabusseau
arXiv preprint arXiv:1712.09520, 2017
192017
A theoretical analysis of catastrophic forgetting through the ntk overlap matrix
T Doan, MA Bennani, B Mazoure, G Rabusseau, P Alquier
International Conference on Artificial Intelligence and Statistics, 1072-1080, 2021
182021
Tensorized random projections
B Rakhshan, G Rabusseau
International Conference on Artificial Intelligence and Statistics, 3306-3316, 2020
132020
Adaptive tensor learning with tensor networks
M Hashemizadeh, M Liu, J Miller, G Rabusseau
arXiv preprint arXiv:2008.05437, 2020
112020
A Tensor Perspective on Weighted Automata, Low-Rank Regression and Algebraic Mixtures
G Rabusseau
Aix-Marseille Université, 2016
112016
Neural architecture search for class-incremental learning
S Huang, V François-Lavet, G Rabusseau
arXiv preprint arXiv:1909.06686, 2019
102019
Clustering-oriented representation learning with attractive-repulsive loss
K Kenyon-Dean, A Cianflone, L Page-Caccia, G Rabusseau, ...
arXiv preprint arXiv:1812.07627, 2018
102018
Recognizable series on hypergraphs
R Bailly, F Denis, G Rabusseau
International Conference on Language and Automata Theory and Applications …, 2015
102015
Multitask spectral learning of weighted automata
G Rabusseau, B Balle, J Pineau
Advances in neural information processing systems 30, 2017
82017
Low-rank approximation of weighted tree automata
G Rabusseau, B Balle, S Cohen
Artificial Intelligence and Statistics, 839-847, 2016
8*2016
Quantum tensor networks, stochastic processes, and weighted automata
S Adhikary, S Srinivasan, J Miller, G Rabusseau, B Boots
International Conference on Artificial Intelligence and Statistics, 2080-2088, 2021
62021
Nonlinear Weighted Finite Automata
T Li, G Rabusseau, D Precup
International Conference on Artificial Intelligence and Statistics, 679-688, 2018
5*2018
Laplacian change point detection for dynamic graphs
S Huang, Y Hitti, G Rabusseau, R Rabbany
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
42020
Randomnet: Towards fully automatic neural architecture design for multimodal learning
S Alletto, S Huang, V Francois-Lavet, Y Nakata, G Rabusseau
arXiv preprint arXiv:2003.01181, 2020
42020
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