Mario Marchand

Publication Activity (10 Years)

Years Active: 1989-2024
Publications (10 Years): 16

Top Topics

Bayesian Analysis

Markov Networks

Top Venues

J. Mach. Learn. Res.

Publications

Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh
Tackling the XAI Disagreement Problem with Regional Explanations. AISTATS (2024)
Qi Chen, Changjian Shui, Ligong Han, Mario Marchand
On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm. NeurIPS (2023)
Qi Chen, Mario Marchand
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. AISTATS (2023)
Qi Chen, Mario Marchand
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. CoRR (2023)
Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh
Fooling SHAP with Stealthily Biased Sampling. ICLR (2023)
Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
Generalization Properties of Decision Trees on Real-valued and Categorical Features. CoRR (2022)
Qi Chen, Changjian Shui, Mario Marchand
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. NeurIPS (2021)
Gabriel Laberge, Yann Pequignot, Foutse Khomh, Mario Marchand, Alexandre Mathieu
Partial order: Finding Consensus among Uncertain Feature Attributions. CoRR (2021)
Qi Chen, Changjian Shui, Mario Marchand
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. CoRR (2021)
Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion. CoRR (2021)
Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
Decision trees as partitioning machines to characterize their generalization properties. CoRR (2020)
Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand
Decision trees as partitioning machines to characterize their generalization properties. NeurIPS (2020)
Prudencio Tossou, Basile Dura, François Laviolette, Mario Marchand, Alexandre Lacoste
Adaptive Deep Kernel Learning. CoRR (2019)
Jean-Francis Roy, Mario Marchand, François Laviolette
A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees. AISTATS (2016)
Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette
Large scale modeling of antimicrobial resistance with interpretable classifiers. CoRR (2016)
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky
Domain-Adversarial Training of Neural Networks. J. Mach. Learn. Res. 17 (2016)
Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Émile Robitaille, Jean-Francis Roy
Efficient Learning of Ensembles with QuadBoost. CoRR (2015)
Sébastien Giguère, François Laviolette, Mario Marchand, Denise M. Tremblay, Sylvain Moineau, Xinxia Liang, Éric Biron, Jacques Corbeil
Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery. PLoS Comput. Biol. 11 (4) (2015)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy
Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm. J. Mach. Learn. Res. 16 (2015)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. CoRR (2015)
Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction. ICML (2015)
Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance. CoRR (2015)
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky
Domain-Adversarial Training of Neural Networks. CoRR (2015)
Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand
Domain-Adversarial Neural Networks. CoRR (2014)
Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle
Agnostic Bayesian Learning of Ensembles. ICML (2014)
Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. NIPS (2014)
Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand
On the String Kernel Pre-Image Problem with Applications in Drug Discovery. CoRR (2014)
Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine. CoRR (2014)
Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette
Sequential Model-Based Ensemble Optimization. UAI (2014)
Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand
Sequential Model-Based Ensemble Optimization. CoRR (2014)
Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. ICML (1) (2013)
Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil
Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC Bioinform. 14 (2013)
Alexandre Lacoste, François Laviolette, Mario Marchand
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets. AISTATS (2012)
Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil
Learning a peptide-protein binding affinity predictor with kernel ridge regression CoRR (2012)
Mohak Shah, Mario Marchand, Jacques Corbeil
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data. IEEE Trans. Pattern Anal. Mach. Intell. 34 (1) (2012)
Jean-Francis Roy, François Laviolette, Mario Marchand
From PAC-Bayes Bounds to Quadratic Programs for Majority Votes. ICML (2011)
Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian
A PAC-Bayes Sample-compression Approach to Kernel Methods. ICML (2011)
François Laviolette, Mario Marchand, Mohak Shah, Sara Shanian
Learning the set covering machine by bound minimization and margin-sparsity trade-off. Mach. Learn. 78 (1-2) (2010)
Mohak Shah, Mario Marchand, Jacques Corbeil
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data CoRR (2010)
Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin
Learning with Randomized Majority Votes. ECML/PKDD (2) (2010)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian
From PAC-Bayes Bounds to KL Regularization. NIPS (2009)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
PAC-Bayesian learning of linear classifiers. ICML (2009)
François Laviolette, Mario Marchand, Sara Shanian
Selective Sampling for Classification. Canadian Conference on AI (2008)
Sébastien Quirion, Chantale Duchesne, Denis Laurendeau, Mario Marchand
Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation. J. WSCG 16 (1-3) (2008)
Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, S. Charles Brubaker, Matthew D. Mullin
Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data. J. Mach. Learn. Res. 8 (2007)
François Laviolette, Mario Marchand
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. J. Mach. Learn. Res. 8 (2007)
Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier. NIPS (2006)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
A PAC-Bayes Risk Bound for General Loss Functions. NIPS (2006)
François Laviolette, Mario Marchand
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. ICML (2005)
François Laviolette, Mario Marchand, Mohak Shah
A PAC-Bayes approach to the Set Covering Machine. NIPS (2005)
Mario Marchand, Marina Sokolova
Learning with Decision Lists of Data-Dependent Features. J. Mach. Learn. Res. 6 (2005)
François Laviolette, Mario Marchand, Mohak Shah
Margin-Sparsity Trade-Off for the Set Covering Machine. ECML (2005)
Mario Marchand, Mohak Shah
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data. NIPS (2004)
Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova
The Set Covering Machine with Data-Dependent Half-Spaces. ICML (2003)
Mario Marchand, John Shawe-Taylor
The Set Covering Machine. J. Mach. Learn. Res. 3 (2002)
Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor
The Decision List Machine. NIPS (2002)
Mario Marchand, John Shawe-Taylor
Learning with the Set Covering Machine. ICML (2001)
Mostefa Golea, Mario Marchand, Thomas R. Hancock
On learning ?-perceptron networks on the uniform distribution. Neural Networks 9 (1) (1996)
Mario Marchand, Saeed Hadjifaradji
Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks. NIPS (1995)
Mario Marchand, Saeed Hadjifaradji
Learning Stochastic Perceptrons Under k-Blocking Distributions. NIPS (1994)
Thomas R. Hancock, Mostefa Golea, Mario Marchand
Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries. Mach. Learn. 16 (3) (1994)
Mostefa Golea, Mario Marchand
Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks. COLT (1993)
Mostefa Golea, Mario Marchand
On Learning Perceptrons with Binary Weights. Neural Comput. 5 (5) (1993)
Mostefa Golea, Mario Marchand
Polynomial Time Algorithms for Learning Neural Nets of NonoverlappingPerceptrons. Comput. Intell. 9 (1993)
Mostefa Golea, Mario Marchand
On learning simple deterministic and probabilistic neural concepts. EuroCOLT (1993)
Mostefa Golea, Mario Marchand, Thomas R. Hancock
On Learning µ-Perceptron Networks with Binary Weights. NIPS (1992)
Pal Rujan, Mario Marchand
Learning by Minimizing Resources in Neural Networks. Complex Syst. 3 (3) (1989)