| Low-Rank Regression with Tensor Responses G Rabusseau, H Kadri Advances in Neural Information Processing Systems, 1867-1875, 2016 | 60 | 2016 |
| 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 | 30 | 2019 |
| 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 | 29 | 2021 |
| 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 | 20 | 2019 |
| 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 | 19 | 2018 |
| Tensor regression networks with various low-rank tensor approximations X Cao, G Rabusseau arXiv preprint arXiv:1712.09520, 2017 | 19 | 2017 |
| 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 | 18 | 2021 |
| Tensorized random projections B Rakhshan, G Rabusseau International Conference on Artificial Intelligence and Statistics, 3306-3316, 2020 | 13 | 2020 |
| Adaptive tensor learning with tensor networks M Hashemizadeh, M Liu, J Miller, G Rabusseau arXiv preprint arXiv:2008.05437, 2020 | 11 | 2020 |
| A Tensor Perspective on Weighted Automata, Low-Rank Regression and Algebraic Mixtures G Rabusseau Aix-Marseille Université, 2016 | 11 | 2016 |
| Neural architecture search for class-incremental learning S Huang, V François-Lavet, G Rabusseau arXiv preprint arXiv:1909.06686, 2019 | 10 | 2019 |
| Clustering-oriented representation learning with attractive-repulsive loss K Kenyon-Dean, A Cianflone, L Page-Caccia, G Rabusseau, ... arXiv preprint arXiv:1812.07627, 2018 | 10 | 2018 |
| Recognizable series on hypergraphs R Bailly, F Denis, G Rabusseau International Conference on Language and Automata Theory and Applications …, 2015 | 10 | 2015 |
| Multitask spectral learning of weighted automata G Rabusseau, B Balle, J Pineau Advances in neural information processing systems 30, 2017 | 8 | 2017 |
| 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 | 6 | 2021 |
| 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 | 4 | 2020 |
| 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 | 4 | 2020 |
Doina PrecupDeepMind and McGill UniversityVerified email at cs.mcgill.ca
