Mario Marchand

75 papers A* 13A 5Journal 40Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Mixtures of Transparent Local Models. CoRR Niffa Cheick Oumar Diaby, Thierry Duchesne, Mario Marchand
2026	J	`jnl`	The geometry of invariant learning: an information-theoretic analysis of data augmentation and generalization. CoRR Abdelali Bouyahia, Frédéric Leblanc, Mario Marchand
2025	J	`jnl`	Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions. Trans. Mach. Learn. Res. Gabriel Dubé, Mario Marchand
2024	A	`conf`	Tackling the XAI Disagreement Problem with Regional Explanations. AISTATS Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh
2024	J	`jnl`	Tighter Risk Bounds for Mixtures of Experts. CoRR Wissam Akretche, Frédéric Leblanc, Mario Marchand
2023	A	`conf`	Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. AISTATS Qi Chen, Mario Marchand
2023	J	`jnl`	Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. CoRR Qi Chen, Mario Marchand
2023	A*	`conf`	Fooling SHAP with Stealthily Biased Sampling. ICLR Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh
2023	A*	`conf`	On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm. NeurIPS Qi Chen, Changjian Shui, Ligong Han, Mario Marchand
2023	J	`jnl`	Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set. J. Mach. Learn. Res. Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand
2022	J	`jnl`	Fooling SHAP with Stealthily Biased Sampling. CoRR Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh
2022	J	`jnl`	Generalization Properties of Decision Trees on Real-valued and Categorical Features. CoRR Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
2021	A*	`conf`	Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. NeurIPS Qi Chen, Changjian Shui, Mario Marchand
2021	J	`jnl`	Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. CoRR Qi Chen, Changjian Shui, Mario Marchand
2021	J	`jnl`	Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion. CoRR Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
2021	J	`jnl`	Partial order: Finding Consensus among Uncertain Feature Attributions. CoRR Gabriel Laberge, Yann Pequignot, Foutse Khomh, Mario Marchand, Alexandre Mathieu
2020	A*	`conf`	Decision trees as partitioning machines to characterize their generalization properties. NeurIPS Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
2020	J	`jnl`	Decision trees as partitioning machines to characterize their generalization properties. CoRR Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
2019	J	`jnl`	Adaptive Deep Kernel Learning. CoRR Prudencio Tossou, Basile Dura, François Laviolette, Mario Marchand, Alexandre Lacoste
2017	—	`ch.`	Domain-Adversarial Training of Neural Networks. Domain Adaptation in Computer Vision Applications Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky
2016	A	`conf`	A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees. AISTATS Jean-Francis Roy, Mario Marchand, François Laviolette
2016	J	`jnl`	Domain-Adversarial Training of Neural Networks. J. Mach. Learn. Res. Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky
2016	J	`jnl`	Large scale modeling of antimicrobial resistance with interpretable classifiers. CoRR Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette
2015	A*	`conf`	Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction. ICML Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand
2015	J	`jnl`	Domain-Adversarial Training of Neural Networks. CoRR Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky
2015	J	`jnl`	Efficient Learning of Ensembles with QuadBoost. CoRR Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Émile Robitaille, Jean-Francis Roy
2015	J	`jnl`	Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance. CoRR Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
2015	J	`jnl`	Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery. PLoS Comput. Biol. Sébastien Giguère, François Laviolette, Mario Marchand, Denise M. Tremblay, Sylvain Moineau, Xinxia Liang, Éric Biron, Jacques Corbeil
2015	J	`jnl`	Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. CoRR Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy
2015	J	`jnl`	Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm. J. Mach. Learn. Res. Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy
2014	A*	`conf`	Agnostic Bayesian Learning of Ensembles. ICML Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle
2014	J	`jnl`	Domain-Adversarial Neural Networks. CoRR Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand
2014	J	`jnl`	Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine. CoRR Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
2014	—	`conf`	Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. NIPS Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor
2014	J	`jnl`	On the String Kernel Pre-Image Problem with Applications in Drug Discovery. CoRR Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand
2014	A	`conf`	Sequential Model-Based Ensemble Optimization. UAI Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette
2014	J	`jnl`	Sequential Model-Based Ensemble Optimization. CoRR Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand
2013	J	`jnl`	Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC Bioinform. Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil
2013	—	`conf`	Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. ICML (1) Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla
2012	A	`conf`	Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets. AISTATS Alexandre Lacoste, François Laviolette, Mario Marchand
2012	J	`jnl`	Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data. IEEE Trans. Pattern Anal. Mach. Intell. Mohak Shah, Mario Marchand, Jacques Corbeil
2012	J	`jnl`	Learning a peptide-protein binding affinity predictor with kernel ridge regression CoRR Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil
2011	A*	`conf`	A PAC-Bayes Sample-compression Approach to Kernel Methods. ICML Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian
2011	A*	`conf`	From PAC-Bayes Bounds to Quadratic Programs for Majority Votes. ICML Jean-Francis Roy, François Laviolette, Mario Marchand
2010	J	`jnl`	Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data CoRR Mohak Shah, Mario Marchand, Jacques Corbeil
2010	J	`jnl`	Learning the set covering machine by bound minimization and margin-sparsity trade-off. Mach. Learn. François Laviolette, Mario Marchand, Mohak Shah, Sara Shanian
2010	—	`conf`	Learning with Randomized Majority Votes. ECML/PKDD (2) Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin
2009	—	`conf`	From PAC-Bayes Bounds to KL Regularization. NIPS Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian
2009	A*	`conf`	PAC-Bayesian learning of linear classifiers. ICML Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
2008	J	`jnl`	Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation. J. WSCG Sébastien Quirion, Chantale Duchesne, Denis Laurendeau, Mario Marchand
2008	—	`conf`	Selective Sampling for Classification. Canadian AI François Laviolette, Mario Marchand, Sara Shanian
2007	J	`jnl`	PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. J. Mach. Learn. Res. François Laviolette, Mario Marchand
2007	J	`jnl`	Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data. J. Mach. Learn. Res. Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, S. Charles Brubaker, Matthew D. Mullin
2006	—	`conf`	A PAC-Bayes Risk Bound for General Loss Functions. NIPS Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
2006	—	`ed.`	Advances in Artificial Intelligence, 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Québec City, Québec, Canada, June 7-9, 2006, Proceedings Canadian AI Luc Lamontagne, Mario Marchand
2006	—	`conf`	PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier. NIPS Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier
2005	—	`conf`	A PAC-Bayes approach to the Set Covering Machine. NIPS François Laviolette, Mario Marchand, Mohak Shah
2005	J	`jnl`	Learning with Decision Lists of Data-Dependent Features. J. Mach. Learn. Res. Mario Marchand, Marina Sokolova
2005	—	`conf`	Margin-Sparsity Trade-Off for the Set Covering Machine. ECML François Laviolette, Mario Marchand, Mohak Shah
2005	A*	`conf`	PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. ICML François Laviolette, Mario Marchand
2004	—	`conf`	PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data. NIPS Mario Marchand, Mohak Shah
2003	A*	`conf`	The Set Covering Machine with Data-Dependent Half-Spaces. ICML Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova
2002	—	`conf`	The Decision List Machine. NIPS Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor
2002	J	`jnl`	The Set Covering Machine. J. Mach. Learn. Res. Mario Marchand, John Shawe-Taylor
2001	A*	`conf`	Learning with the Set Covering Machine. ICML Mario Marchand, John Shawe-Taylor
1996	J	`jnl`	On learning ?-perceptron networks on the uniform distribution. Neural Networks Mostefa Golea, Mario Marchand, Thomas R. Hancock
1995	—	`conf`	Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks. NIPS Mario Marchand, Saeed Hadjifaradji
1994	J	`jnl`	Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries. Mach. Learn. Thomas R. Hancock, Mostefa Golea, Mario Marchand
1994	—	`conf`	Learning Stochastic Perceptrons Under k-Blocking Distributions. NIPS Mario Marchand, Saeed Hadjifaradji
1993	A*	`conf`	Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks. COLT Mostefa Golea, Mario Marchand
1993	J	`jnl`	On Learning Perceptrons with Binary Weights. Neural Comput. Mostefa Golea, Mario Marchand
1993	—	`conf`	On learning simple deterministic and probabilistic neural concepts. EuroCOLT Mostefa Golea, Mario Marchand
1993	J	`jnl`	Polynomial Time Algorithms for Learning Neural Nets of NonoverlappingPerceptrons. Comput. Intell. Mostefa Golea, Mario Marchand
1992	—	`conf`	On Learning µ-Perceptron Networks with Binary Weights. NIPS Mostefa Golea, Mario Marchand, Thomas R. Hancock
1989	J	`jnl`	Learning by Minimizing Resources in Neural Networks. Complex Syst. Pal Rujan, Mario Marchand