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Decision trees as partitioning machines to characterize their generalization properties

Oct 14, 2020
Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand

* 9 pages, 5 appendices 

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Adaptive Deep Kernel Learning

May 28, 2019
Prudencio Tossou, Basile Dura, Francois Laviolette, Mario Marchand, Alexandre Lacoste

* Submitted at NeurIPS 2019 

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Large scale modeling of antimicrobial resistance with interpretable classifiers

Dec 03, 2016
Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette

* Peer-reviewed and accepted for presentation at the Machine Learning for Health Workshop, NIPS 2016, Barcelona, Spain 

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Domain-Adversarial Training of Neural Networks

May 26, 2016
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky

* Journal of Machine Learning Research 2016, vol. 17, p. 1-35 
* Published in JMLR: http://jmlr.org/papers/v17/15-239.html 

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Efficient Learning of Ensembles with QuadBoost

Nov 20, 2015
Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Emile Robitaille, Jean-Francis Roy

* 9 pages 

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Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

Jul 28, 2015
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy

* Journal of Machine Learning Research 2015, vol. 16, p. 787-860 
* Published in JMLR http://jmlr.org/papers/v16/germain15a.html 

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Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance

May 22, 2015
Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil

* Peer-reviewed and accepted for an oral presentation in the Greed is Great workshop at the International Conference on Machine Learning, Lille, France, 2015 

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Domain-Adversarial Neural Networks

Feb 09, 2015
Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand

* The first version of this paper was accepted at the "Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice" (NIPS 2014, Montreal, Canada). See: https://sites.google.com/site/multitaskwsnips2014/ 

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On the String Kernel Pre-Image Problem with Applications in Drug Discovery

Dec 04, 2014
Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand

* Peer-reviewed and accepted for presentation at Machine Learning in Computational Biology 2014, Montr\'eal, Qu\'ebec, Canada 

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Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine

Dec 02, 2014
Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil

* Presented at Machine Learning in Computational Biology 2014, Montr\'eal, Qu\'ebec, Canada 

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A Note on Improved Loss Bounds for Multiple Kernel Learning

May 12, 2014
Zakria Hussain, John Shawe-Taylor, Mario Marchand

* Extended proof 

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Sequential Model-Based Ensemble Optimization

Feb 04, 2014
Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand


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Learning a peptide-protein binding affinity predictor with kernel ridge regression

Jul 31, 2012
Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil

* BMC Bioinformatics 2013, 14:82 
* 22 pages, 4 figures, 5 tables 

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Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data

May 04, 2010
Mohak Shah, Mario Marchand, Jacques Corbeil


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