Laviolette, François

37 publications

MLOSS 2022 Toolbox for Multimodal Learn (scikit-Multimodallearn) Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette

MLJ 2020 Fast Greedy C-Bound Minimization with Guarantees Baptiste Bauvin, Cécile Capponi, Jean-Francis Roy, François Laviolette

UAI 2020 The Indian Chefs Process Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Marcel Gerven, François Laviolette

NeurIPS 2019 Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks Gaël Letarte, Pascal Germain, Benjamin Guedj, Francois Laviolette

NeurIPS 2017 Maximum Margin Interval Trees Alexandre Drouin, Toby Hocking, Francois Laviolette

AISTATS 2016 A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees Jean-Francis Roy, Mario Marchand, François Laviolette

ICML 2016 A New PAC-Bayesian Perspective on Domain Adaptation Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

JMLR 2016 Domain-Adversarial Training of Neural Networks Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, Victor Lempitsky

AISTATS 2016 PAC-Bayesian Bounds Based on the Rényi Divergence Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

ICML 2015 Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction Sébastien Giguère, Amélie Rolland, Francois Laviolette, Mario Marchand

JMLR 2015 Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario March, Jean-Francis Roy

ICML 2014 Agnostic Bayesian Learning of Ensembles Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle

AISTATS 2014 PAC-Bayesian Theory for Transductive Learning Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

UAI 2014 Sequential Model-Based Ensemble Optimization Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette

ICML 2013 A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

IJCAI 2013 Accelerated Robust Point Cloud Registration in Natural Environments Through Positive and Unlabeled Learning Maxime Latulippe, Alexandre Drouin, Philippe Giguère, François Laviolette

ICML 2013 Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla

AISTATS 2012 Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets Alexandre Lacoste, Francois Laviolette, Mario Marchand

UAI 2012 PAC-Bayesian Inequalities for Martingales Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer

ICML 2011 A PAC-Bayes Sample-Compression Approach to Kernel Methods Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian

ICML 2011 From PAC-Bayes Bounds to Quadratic Programs for Majority Votes Jean-Francis Roy, François Laviolette, Mario Marchand

NeurIPS 2011 PAC-Bayesian Analysis of Contextual Bandits Yevgeny Seldin, Peter Auer, John S. Shawe-taylor, Ronald Ortner, François Laviolette

ALT 2010 Distribution-Dependent PAC-Bayes Priors Guy Lever, François Laviolette, John Shawe-Taylor

MLJ 2010 Learning the Set Covering Machine by Bound Minimization and Margin-Sparsity Trade-Off François Laviolette, Mario Marchand, Mohak Shah, Sara Shanian

ECML-PKDD 2010 Learning with Randomized Majority Votes Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin

NeurIPS 2009 From PAC-Bayes Bounds to KL Regularization Pascal Germain, Alexandre Lacasse, Mario Marchand, Sara Shanian, François Laviolette

ECML-PKDD 2009 Learning the Difference Between Partially Observable Dynamical Systems Sami Zhioua, Doina Precup, François Laviolette, Josée Desharnais

ICML 2009 PAC-Bayesian Learning of Linear Classifiers Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand

NeurIPS 2008 A Transductive Bound for the Voted Classifier with an Application to Semi-Supervised Learning Massih R. Amini, Nicolas Usunier, François Laviolette

JMLR 2007 PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers François Laviolette, Mario Marchand

JMLR 2007 Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, Spencer Charles Brubaker, Matthew D. Mullin

NeurIPS 2006 A PAC-Bayes Risk Bound for General Loss Functions Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand

ECML-PKDD 2006 A Selective Sampling Strategy for Label Ranking Massih-Reza Amini, Nicolas Usunier, François Laviolette, Alexandre Lacasse, Patrick Gallinari

NeurIPS 2006 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier

NeurIPS 2005 A PAC-Bayes Approach to the Set Covering Machine François Laviolette, Mario Marchand, Mohak Shah

ECML-PKDD 2005 Margin-Sparsity Trade-Off for the Set Covering Machine François Laviolette, Mario Marchand, Mohak Shah

ICML 2005 PAC-Bayes Risk Bounds for Sample-Compressed Gibbs Classifiers François Laviolette, Mario Marchand