I'm currently Research Scientist at AT&T Labs. I am mainly interested in machine learning applications, especially advertisement, customer modeling, search, information retrieval, and music.
Some Publications
Xiaoxiao Shi, Jean-Francois Paiement, David Grangier, and Philip S. Yu.
Learning from Heterogeneous Sources via Gradient Boosting Consensus. SIAM International Conference on Data Mining, 2012 (top 10 best papers).
J. Arguello, F. Diaz, and J.-F. Paiement.
Vertical Selection in the Presence of Unlabeled Verticals. Proceedings of the 33rd Annual ACM SIGIR Conference, 2010.
J.-F. Paiement, S. Bengio, and D. Eck.
Probabilistic Models for Melodic Prediction. Artificial Intelligence, 173 (14):1266-1274, Elsevier, 2009.
J.-F. Paiement, S. Bengio, and D. Eck.
Probabilistic Models for Melodic Prediction. Artificial Intelligence, 173 (14):1266-1274, Elsevier, 2009.
J.-F. Paiement, Y. Grandvalet, and S. Bengio. Predictive Models for Music. Connection Science special issue on "Music, Brain, & Cognition", 21 (2-3):253-272, Taylor & Francis, 2009.
J.-F. Paiement, Y. Grandvalet, S. Bengio, and D. Eck. A Distance Model for Rhythms. Proceedings of the 25th International Conference on Machine
Learning (ICML), 2008.
J.-F. Paiement, D. Eck, and S. Bengio. Probabilistic Melodic Harmonization. Proceedings of the 19th Canadian Conference on Artificial
Intelligence, 2006.
J.-F. Paiement, D. Eck, and S. Bengio. A Probabilistic Model for Chord
Progressions. Proceedings of the 6th International Conference on Music
Information Retrieval (ISMIR), 2005.
Y. Bengio, P. Vincent, J.-F. Paiement, O. Delalleau, M. Ouimet, and N.
Le Roux. Spectral
Clustering and Kernel PCA are Learning Eigenfunctions.
Technical Report 1239, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2003.