Mario Marchand

According to our database, Mario Marchand authored at least 61 papers between 1989 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Tackling the XAI Disagreement Problem with Regional Explanations.

Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set.

Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand

J. Mach. Learn. Res., 2023

On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm.

Qi Chen, Changjian Shui, Ligong Han, Mario Marchand

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fooling SHAP with Stealthily Biased Sampling.

Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation.

Qi Chen, Mario Marchand

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Generalization Properties of Decision Trees on Real-valued and Categorical Features.

Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand

CoRR, 2022

2021

Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion.

Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand

CoRR, 2021

Partial order: Finding Consensus among Uncertain Feature Attributions.

Gabriel Laberge, Yann Pequignot, Foutse Khomh, Mario Marchand, Alexandre Mathieu

CoRR, 2021

Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis.

Qi Chen, Changjian Shui, Mario Marchand

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Decision trees as partitioning machines to characterize their generalization properties.

Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Adaptive Deep Kernel Learning.

Prudencio Tossou, Basile Dura, François Laviolette, Mario Marchand, Alexandre Lacoste

CoRR, 2019

2017

Domain-Adversarial Training of Neural Networks.

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky

Proceedings of the Domain Adaptation in Computer Vision Applications., 2017

2016

Domain-Adversarial Training of Neural Networks.

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky

J. Mach. Learn. Res., 2016

Large scale modeling of antimicrobial resistance with interpretable classifiers.

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

CoRR, 2016

A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees.

Jean-Francis Roy, Mario Marchand, François Laviolette

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery.

Sébastien Giguère, François Laviolette, Mario Marchand, Denise M. Tremblay, Sylvain Moineau, Xinxia Liang, Éric Biron, Jacques Corbeil

PLoS Comput. Biol., 2015

Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm.

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

J. Mach. Learn. Res., 2015

Efficient Learning of Ensembles with QuadBoost.

Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Émile Robitaille, Jean-Francis Roy

CoRR, 2015

Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance.

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

CoRR, 2015

Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction.

Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

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

Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand

CoRR, 2014

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

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

CoRR, 2014

Domain-Adversarial Neural Networks.

Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand

CoRR, 2014

Sequential Model-Based Ensemble Optimization.

Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks.

Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Agnostic Bayesian Learning of Ensembles.

Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

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

Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil

BMC Bioinform., 2013

Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction.

Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

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

Mohak Shah, Mario Marchand, Jacques Corbeil

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets.

Alexandre Lacoste, François Laviolette, Mario Marchand

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

2011

From PAC-Bayes Bounds to Quadratic Programs for Majority Votes.

Jean-Francis Roy, François Laviolette, Mario Marchand

Proceedings of the 28th International Conference on Machine Learning, 2011

A PAC-Bayes Sample-compression Approach to Kernel Methods.

Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Learning the set covering machine by bound minimization and margin-sparsity trade-off.

François Laviolette, Mario Marchand, Mohak Shah, Sara Shanian

Mach. Learn., 2010

Learning with Randomized Majority Votes.

Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

2009

From PAC-Bayes Bounds to KL Regularization.

Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

PAC-Bayesian learning of linear classifiers.

Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation.

Sébastien Quirion, Chantale Duchesne, Denis Laurendeau, Mario Marchand

J. WSCG, 2008

Selective Sampling for Classification.

François Laviolette, Mario Marchand, Sara Shanian

Proceedings of the Advances in Artificial Intelligence , 2008

2007

PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers.

François Laviolette, Mario Marchand

J. Mach. Learn. Res., 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, S. Charles Brubaker, Matthew D. Mullin

J. Mach. Learn. Res., 2007

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

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

A PAC-Bayes Risk Bound for General Loss Functions.

Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005

Learning with Decision Lists of Data-Dependent Features.

Mario Marchand, Marina Sokolova

J. Mach. Learn. Res., 2005

A PAC-Bayes approach to the Set Covering Machine.

François Laviolette, Mario Marchand, Mohak Shah

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers.

François Laviolette, Mario Marchand

Proceedings of the Machine Learning, 2005

Margin-Sparsity Trade-Off for the Set Covering Machine.

François Laviolette, Mario Marchand, Mohak Shah

Proceedings of the Machine Learning: ECML 2005, 2005

2004

PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data.

Mario Marchand, Mohak Shah

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

The Set Covering Machine with Data-Dependent Half-Spaces.

Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova

Proceedings of the Machine Learning, 2003

2002

The Set Covering Machine.

Mario Marchand, John Shawe-Taylor

J. Mach. Learn. Res., 2002

The Decision List Machine.

Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Learning with the Set Covering Machine.

Mario Marchand, John Shawe-Taylor

Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

1996

On learning ?-perceptron networks on the uniform distribution.

Mostefa Golea, Mario Marchand, Thomas R. Hancock

Neural Networks, 1996

1995

Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks.

Mario Marchand, Saeed Hadjifaradji

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994

Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries.

Thomas R. Hancock, Mostefa Golea, Mario Marchand

Mach. Learn., 1994

Learning Stochastic Perceptrons Under k-Blocking Distributions.

Mario Marchand, Saeed Hadjifaradji

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

1993

On Learning Perceptrons with Binary Weights.

Mostefa Golea, Mario Marchand

Neural Comput., 1993

Polynomial Time Algorithms for Learning Neural Nets of NonoverlappingPerceptrons.

Mostefa Golea, Mario Marchand

Comput. Intell., 1993

On learning simple deterministic and probabilistic neural concepts.

Mostefa Golea, Mario Marchand

Proceedings of the First European Conference on Computational Learning Theory, 1993

Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks.

Mostefa Golea, Mario Marchand

Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, 1993

1992

On Learning µ-Perceptron Networks with Binary Weights.

Mostefa Golea, Mario Marchand, Thomas R. Hancock

Proceedings of the Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30, 1992

1989

Learning by Minimizing Resources in Neural Networks.

Pal Rujan, Mario Marchand

Complex Syst., 1989