- Jacques Corbeil [ DBLP ]
- Alexandre Drouin [ DBLP]
- Pascal Germain [ DBLP ]
- Alexandre Lacoste [ Research Gate | DBLP ]
- Luc Lamontagne [ DBLP ]
- François Laviolette [ DBLP ]
- Mario Marchand [ DBLP]
- Jean-Francis Roy [ DBLP ]
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand and Jean-Francis Roy. Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. Journal of Machine Learning Research (JMLR); 16(Apr):787-860, 2015.
Yarslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand and Victor Lempitsky. Domain-Adversarial Training of Neural Networks. Pre-print arXiv:1505.07818. (2015).
Luc Bégin, Pascal Germain, François Laviolette and Jean-Francis Roy. PAC-Bayesian Theory for Transductive Learning. International Conference on Artificial Intelligence and Statistics (AISTATS), 2014.
Sébastien Giguère, François Laviolette, Mario Marchand, Denise Tremblay, Sylvain Moineau, Éric Biron and Jacques Corbeil. Improved design and screening of high bioactivity peptides for drug discovery. Under Review.
Sébastien Giguère, Alexandre Drouin, Alexandre Lacoste, Mario Marchand, Jacques Corbeil, François Laviolette. MHC-NP: Predicting Peptides Naturally Processed by the MHC. Journal of Immunological Methods, 2013, vol. 400, p. 30-36.
[ pdf ]
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. In ICML 2013.
Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla. Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. In ICML 2013.
Maxime Latulippe, Alexandre Drouin, Philippe Giguere, and François Laviolette. Accelerated Robust Point Cloud Registration in Natural Environments through Positive and Unlabeled Learning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2013) 2013.
[ pdf ]
Sébastien Giguère, Mario Marchand, François Laviolette, Jacques Corbeil, and Alexandre Drouin. Learning a Peptide-Protein Binding Affinity Predictor with Kernel Ridge Regression. BMC Bioinformatics, 2013, vol. 14, no 1, p. 82.
Pascal Germain, Sébastien Giguère, Jean-Francis Roy, Brice Zirakiza, François Laviolette, and Claude-Guy Quimper. A Pseudo-Boolean Set Covering Machine. In Proceedings of the 18th International Conference on Principles and Practice of Constraint Programming (CP 12), pages 916-924, 2012.
François Laviolette, Mario Marchand, and Jean-Francis Roy. From PAC-Bayes Bounds to Quadratic Programs for Majority Votes. In ICML 2011.
Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, and Sara Shanian. A PAC-Bayes Sample Compression Approach to Kernel Methods. In ICML 2011.
A. Lacasse, F. Laviolette, M. Marchand, and F. Turgeon-Boutin. Learning with Randomized Majority Votes. Machine Learning and Knowledge Discovery in Databases, pages 162-177, 2010.
F. Laviolette, M. Marchand, M. Shah, and S. Shanian. Learning the set covering machine by bound minimization and margin-sparsity trade-off. Machine Learning 78(1-2): 175-201 (2010)
Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario Marchand, and Sara Shanian. From PAC-Bayes bounds to KL regularization. In NIPS 2009.
Pascal Germain, Alexandre Lacasse, Francois Laviolette, and Mario Marchand. PAC-Bayesian learning of linear classifiers. In ICML 2009.
S. Boisvert, M. Marchand, F. Laviolette, and J. Corbeil. HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels. Retrovirology, 5(1):110, 2008.
[ http ]
F. Laviolette, M. Marchand, and S. Shanian.Selective Sampling for Classification. Lecture Notes in Computer Science, 5032:191, 2008.
Z. Hussain, F. Laviolette, M. Marchand, J. Shawe-Taylor, S.C. Brubaker, and M.D. Mullin. Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data. The Journal of Machine Learning Research, 8:2533-2549, 2007.
F. Laviolette and M. Marchand. PAC-Bayes risk bounds for stochastic averages and majority votes of sample-compressed classifiers. Journal of Machine Learning Research, 8:1461-1487, 2007.
Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, and Nicolas Usunier. PAC-Bayes bounds for the risk of the majority vote and the variance of the Gibbs classifier. In NIPS 2006.
Pascal Germain, Alexandre Lacasse, François Laviolette, and Mario Marchand. PAC-Bayes risk bounds for general loss functions. In NIPS 2006.
François Laviolette, Mario Marchand, and Mohak Shah. A PAC-Bayes approach to the set covering machine. In NIPS 2005.
François Laviolette and Mario Marchand. PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. In ICML 2005.
François Laviolette, Mario Marchand, and Mohak Shah. Margin-sparsity trade-off for the set covering machine. Proceedings of the 16th European Conference on Machine Learning (ECML 2005); Lecture Notes in Artificial Intelligence, 3720:206-217, 2005.
Mario Marchand and Marina Sokolova. Learning with decision lists of data-dependent features. Journal of Machine Learning Reasearch, 6:427-451, 2005.
Mario Marchand and Mohak Shah. PAC-Bayes learning of conjunctions and classification of gene-expression data. In NIPS 2004.
Mario Marchand, Mohak Shah, John Shawe-Taylor, and Marina Sokolova. The set covering machine with data-dependent half-spaces. In ICML 2003.
François Laviolette. Big data analytics, and its tool of predilection: Machine Learning, 2016. [slides]