Articles in Peer-Reviewed Journals

V. François-Lavet, G. Rabusseau, J. Pineau, D. Ernst, and R. Fonteneau. On overfitting and asymptotic bias in batch reinforcement learning with partial observability. Journal of Artificial Intelligence Research, 65:1--30, 2019. [ bib | arXiv ]
R. Bailly, G. Rabusseau, and F. Denis. Recognizable series on graphs and hypergraphs. Journal of Computer and System Sciences, 104:58 -- 81, 2019. [ bib | preprint | pdf ]
B. Balle and G. Rabusseau. Approximate minimization of weighted tree automata. Information and Computation, 2019 (in press). [ bib | pdf ]

Articles in Peer-Reviewed International Conferences

G. Rabusseau, T. Li, and D. Precup. Connecting weighted automata and recurrent neural networks through spectral learning. In AISTATS. 2019. [ bib | arXiv | pdf | poster | slides ]
P. Amortila and G. Rabusseau. Learning graph weighted models on pictures. ICGI, 2018. [ bib | arXiv | pdf ]
T. Li, G. Rabusseau, and D. Precup. Nonlinear weighted finite automata. In International Conference on Artificial Intelligence and Statistics, pages 679--688. 2018. [ bib | pdf ]
G. Rabusseau. Minimization of graph weighted models over circular strings. In International Conference on Foundations of Software Science and Computation Structures, pages 513--529. 2018. [ bib | pdf | slides ]
G. Rabusseau, B. Balle, and J. Pineau. Multitask spectral learning of weighted automata. In Advances in Neural Information Processing Systems 30, pages 2585--2594. 2017. [ bib | pdf | poster | slides ]
M. Ruffini, G. Rabusseau, and B. Balle. Hierarchical methods of moments. In Advances in Neural Information Processing Systems 30, pages 1899--1908. 2017. [ bib | pdf ]
G. Rabusseau and H. Kadri. Low-rank regression with tensor responses. In Advances In Neural Information Processing Systems 29, pages 1867--1875. 2016. [ bib | code | pdf | poster ]
G. Rabusseau, B. Balle, and S. B. Cohen. Low-Rank Approximation of Weighted Tree Automata. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, pages 839--847, 2016. [ bib | arXiv | pdf | poster ]
R. Bailly, F. Denis, and G. Rabusseau. Recognizable Series on Hypergraphs. In Proceedings of the 9th International Conference on Language and Automata Theory and Applications, pages 639--651, 2015. [ bib | arXiv | slides ]
G. Rabusseau and F. Denis. Maximizing a Tree Series in the Representation Space. In Proceedings of the 12th International Conference on Grammatical Inference, pages 124--138, 2014. [ bib | pdf | slides ]

Articles in Peer-Reviewed French Conferences

G. Rabusseau. Régression de faible rang pour réponses tensorielles. Conférence sur l'Apprentissage Automatique, 2016. [ bib ]
G. Rabusseau, B. Balle, and S. B. Cohen. Minimisation approximée d'automates pondérés d'arbres. Conférence sur l'Apprentissage Automatique, 2016. [ bib ]
G. Rabusseau, H. Kadri, and F. Denis. Régression de faible rang non-paramétrique pour réponses tensorielles. Colloque International Francophone de Traitement du Signal et de l'Image, 2015. [ bib ]
G. Rabusseau and F. Denis. Décompositions Tensorielles pour l'Apprentissage de Modèles de Mélanges Négatifs. Conférence sur l'Apprentissage Automatique, 2014. Best paper award. [ bib | slides ]

Workshop Contributions

K. Kenyon-Dean, A. Cianflone, L. Page-Caccia, G. Rabusseau, J. C. K. Cheung, and D. Precup. Clustering-oriented representation learning with attractive-repulsive loss. AAAI 2019 Workshop on Network Interpretability for Deep Learning, 2019. [ bib | arXiv ]
D. Wu, G. Rabusseau, V. François-lavet, D. Precup, and B. Boulet. Optimizing home energy management and electric vehicle charging with reinforcement learning. Adaptive Learning Agents (ALA) workshop at the Federated AI Meeting, 2018. [ bib | pdf ]
S. Huang, V. François-Lavet, G. Rabusseau, and J. Pineau. Exploring continual learning using incremental architecture search. NIPS 2018 Workshop on Continual Learning, 2018. [ bib | pdf ]
P. Amortila and G. Rabusseau. Learning graph weighted models on pictures. 2nd workshop on Learning and Automata (LearnAut at FLoC 2018), 2018. [ bib ]
G. Rabusseau and J. Pineau. Multitask spectral learning of weighted automata. LICS workshop on Learning and Automata, 2017. [ bib ]
T. Li, G. Rabusseau, and D. Precup. Neural network based nonlinear weighted finite automata. LICS workshop on Learning and Automata, 2017. [ bib ]
R. Bailly and G. Rabusseau. Graph learning as a tensor factorization problem. NIPS workshop on Learning with Tensors, 2016. [ bib | pdf ]
G. Rabusseau and F. Denis. Learning Negative Mixture Models by Tensor Decompositions. Workshop on Method of Moments and Spectral Learning (ICML 2014), 2014. [ bib | poster ]

Technical Reports / Preprints

E. Crawford, G. Rabusseau, and J. Pineau. Sequential coordination of deep models for learning visual arithmetic. 2018. [ bib | pdf ]
X. Cao and G. Rabusseau. Tensor regression networks with various low-rank tensor approximations. arXiv preprint arXiv:1712.09520, 2017. [ bib | arXiv ]
G. Rabusseau and F. Denis. Learning Negative Mixture Models by Tensor Decompositions. CoRR, abs/1403.4224, 2014. [ bib | arXiv ]

Thesis

G. Rabusseau. A Tensor Perspective on Weighted Automata, Low-Rank Regression and Algebraic Mixtures. PhD thesis, Aix-Marseille Université, 2016. [ bib | pdf ]