Articles in Peer-Reviewed International Conferences

G. Rabusseau, B. Balle, and J. Pineau. Multitask spectral learning of weighted automata. NIPS, 2017. [ bib | preprint ]
M. Ruffini, B. Balle, and G. Rabusseau. Hierarchical method of moments. NIPS, 2017. [ bib ]
G. Rabusseau and H. Kadri. Low-rank regression with tensor responses. In Advances In Neural Information Processing Systems 29, NIPS 2016, 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, AISTATS 2016, 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, LATA 2015, pages 639--651, 2015. Journal version submitted to Journal of Computer and System Sciences. [ 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, ICGI 2014, 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, CAP 2016, 2016. [ bib ]
G. Rabusseau, B. Balle, and S. B. Cohen. Minimisation approximée d'automates pondérés d'arbres. Conférence sur l'Apprentissage Automatique, CAP 2016, 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, GRETSI 2015, 2015. [ bib ]
G. Rabusseau and F. Denis. Décompositions Tensorielles pour l'Apprentissage de Modèles de Mélanges Négatifs. Conférence sur l'Apprentissage Automatique, CAP 2014, 2014. Best paper award. [ bib | slides ]

Workshop Contributions

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 | pdf ]
R. Bailly and G. Rabusseau. Graph learning as a tensor factorization problem. NIPS workshop on Learning with Tensors, 2016. [ bib ]
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

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 ]