Marchand, Mario

40 publications

TMLR 2025 Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions Gabriel Dubé, Mario Marchand

AISTATS 2024 Tackling the XAI Disagreement Problem with Regional Explanations Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh

AISTATS 2023 Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation Qi Chen, Mario Marchand

ICLR 2023 Fooling SHAP with Stealthily Biased Sampling Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh

NeurIPS 2023 On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm Qi Chen, Changjian Shui, Ligong Han, Mario Marchand

JMLR 2023 Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand

NeurIPS 2021 Generalization Bounds for Meta-Learning: An Information-Theoretic Analysis Qi Chen, Changjian Shui, Mario Marchand

NeurIPS 2020 Decision Trees as Partitioning Machines to Characterize Their Generalization Properties Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand

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

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

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

NeurIPS 2014 Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S Shawe-Taylor

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

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

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

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

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

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

JMLR 2005 Learning with Decision Lists of Data-Dependent Features Mario Marchand, Marina Sokolova

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

NeurIPS 2004 PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data Mario Marchand, Mohak Shah

ICML 2003 The Set Covering Machine with Data-Dependent Half-Spaces Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova

NeurIPS 2002 The Decision List Machine Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John S. Shawe-taylor

JMLR 2002 The Set Covering Machine Mario Marchand, John Shawe-Taylor

ICML 2001 Learning with the Set Covering Machine Mario Marchand, John Shawe-Taylor

NeurIPS 1995 Strong Unimodality and Exact Learning of Constant Depth Μ-Perceptron Networks Mario Marchand, Saeed Hadjifaradji

MLJ 1994 Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries Thomas R. Hancock, Mostefa Golea, Mario Marchand

NeurIPS 1994 Learning Stochastic Perceptrons Under K-Blocking Distributions Mario Marchand, Saeed Hadjifaradji

COLT 1993 Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks Mostefa Golea, Mario Marchand

NeCo 1993 On Learning Perceptrons with Binary Weights Mostefa Golea, Mario Marchand

NeurIPS 1992 On Learning Μ-Perceptron Networks with Binary Weights Mostefa Golea, Mario Marchand, Thomas R. Hancock