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ORCIDMario Marchand
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- affiliation: Université Laval
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Journal Articles
- 2023
[j17]Gabriel Laberge, Yann Pequignot, Alexandre Mathieu, Foutse Khomh, Mario Marchand:
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set. J. Mach. Learn. Res. 24: 364:1-364:50 (2023)- 2016
- [j16]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: 59:1-59:35 (2016) - 2015
- [j15]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: 787-860 (2015) - [j14]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) - 2013
- [j13]Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil:
Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC Bioinform. 14: 82 (2013) - 2012
- [j12]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): 174-186 (2012) - 2010
- [j11]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): 175-201 (2010) - 2008
- [j10]Sébastien Quirion, Chantale Duchesne, Denis Laurendeau, Mario Marchand:
Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation. J. WSCG 16(1-3): 41-48 (2008) - 2007
- [j9]François Laviolette, Mario Marchand:
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. J. Mach. Learn. Res. 8: 1461-1487 (2007) - [j8]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: 2533-2549 (2007) - 2005
- [j7]Mario Marchand, Marina Sokolova:
Learning with Decision Lists of Data-Dependent Features. J. Mach. Learn. Res. 6: 427-451 (2005) - 2002
- [j6]Mario Marchand, John Shawe-Taylor:
The Set Covering Machine. J. Mach. Learn. Res. 3: 723-746 (2002) - 1996
- [j5]Mostefa Golea, Mario Marchand, Thomas R. Hancock:
On learning ?-perceptron networks on the uniform distribution. Neural Networks 9(1): 67-82 (1996) - 1994
- [j4]Thomas R. Hancock, Mostefa Golea, Mario Marchand:
Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries. Mach. Learn. 16(3): 161-183 (1994) - 1993
- [j3]Mostefa Golea, Mario Marchand:
Polynomial Time Algorithms for Learning Neural Nets of NonoverlappingPerceptrons. Comput. Intell. 9: 155-170 (1993) - [j2]Mostefa Golea, Mario Marchand:
On Learning Perceptrons with Binary Weights. Neural Comput. 5(5): 767-782 (1993) - 1989
- [j1]Pal Rujan, Mario Marchand:
Learning by Minimizing Resources in Neural Networks. Complex Syst. 3(3) (1989)
Conference and Workshop Papers
- 2024
- [c33]Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh:
Tackling the XAI Disagreement Problem with Regional Explanations. AISTATS 2024: 2017-2025 - 2023
- [c32]Qi Chen, Mario Marchand:
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. AISTATS 2023: 10368-10394 - [c31]Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh:
Fooling SHAP with Stealthily Biased Sampling. ICLR 2023 - [c30]Qi Chen, Changjian Shui, Ligong Han, Mario Marchand:
On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm. NeurIPS 2023 - 2021
- [c29]Qi Chen, Changjian Shui, Mario Marchand:
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. NeurIPS 2021: 25878-25890 - 2020
- [c28]Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand:
Decision trees as partitioning machines to characterize their generalization properties. NeurIPS 2020 - 2016
- [c27]Jean-Francis Roy, Mario Marchand, François Laviolette:
A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees. AISTATS 2016: 1241-1249 - 2015
- [c26]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: 2021-2029 - 2014
- [c25]Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle:
Agnostic Bayesian Learning of Ensembles. ICML 2014: 611-619 - [c24]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: 873-881 - [c23]Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette:
Sequential Model-Based Ensemble Optimization. UAI 2014: 440-448 - 2013
- [c22]Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla:
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. ICML (1) 2013: 107-114 - 2012
- [c21]Alexandre Lacoste, François Laviolette, Mario Marchand:
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets. AISTATS 2012: 665-675 - 2011
- [c20]Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian:
A PAC-Bayes Sample-compression Approach to Kernel Methods. ICML 2011: 297-304 - [c19]Jean-Francis Roy, François Laviolette, Mario Marchand:
From PAC-Bayes Bounds to Quadratic Programs for Majority Votes. ICML 2011: 649-656 - 2010
- [c18]Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin:
Learning with Randomized Majority Votes. ECML/PKDD (2) 2010: 162-177 - 2009
- [c17]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
PAC-Bayesian learning of linear classifiers. ICML 2009: 353-360 - [c16]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian:
From PAC-Bayes Bounds to KL Regularization. NIPS 2009: 603-610 - 2008
- [c15]François Laviolette, Mario Marchand, Sara Shanian:
Selective Sampling for Classification. Canadian AI 2008: 191-202 - 2006
- [c14]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
A PAC-Bayes Risk Bound for General Loss Functions. NIPS 2006: 449-456 - [c13]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: 769-776 - 2005
- [c12]François Laviolette, Mario Marchand, Mohak Shah:
Margin-Sparsity Trade-Off for the Set Covering Machine. ECML 2005: 206-217 - [c11]François Laviolette, Mario Marchand:
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. ICML 2005: 481-488 - [c10]François Laviolette, Mario Marchand, Mohak Shah:
A PAC-Bayes approach to the Set Covering Machine. NIPS 2005: 731-738 - 2004
- [c9]Mario Marchand, Mohak Shah:
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data. NIPS 2004: 881-888 - 2003
- [c8]Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova:
The Set Covering Machine with Data-Dependent Half-Spaces. ICML 2003: 520-527 - 2002
- [c7]Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor:
The Decision List Machine. NIPS 2002: 921-928 - 2001
- [c6]Mario Marchand, John Shawe-Taylor:
Learning with the Set Covering Machine. ICML 2001: 345-352 - 1995
- [c5]Mario Marchand, Saeed Hadjifaradji:
Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks. NIPS 1995: 288-294 - 1994
- [c4]Mario Marchand, Saeed Hadjifaradji:
Learning Stochastic Perceptrons Under k-Blocking Distributions. NIPS 1994: 279-286 - 1993
- [c3]Mostefa Golea, Mario Marchand:
Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks. COLT 1993: 151-157 - [c2]Mostefa Golea, Mario Marchand:
On learning simple deterministic and probabilistic neural concepts. EuroCOLT 1993: 47-60 - 1992
- [c1]Mostefa Golea, Mario Marchand, Thomas R. Hancock:
On Learning µ-Perceptron Networks with Binary Weights. NIPS 1992: 591-598
Parts in Books or Collections
- 2017
- [p1]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. Domain Adaptation in Computer Vision Applications 2017: 189-209
Editorship
- 2006
- [e1]Luc Lamontagne, Mario Marchand:
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. Lecture Notes in Computer Science 4013, Springer 2006, ISBN 3-540-34628-7 [contents]
Informal and Other Publications
- 2024
- [i19]Wissam Akretche, Frédéric Leblanc, Mario Marchand:
Tighter Risk Bounds for Mixtures of Experts. CoRR abs/2410.10397 (2024) - 2023
- [i18]Qi Chen, Mario Marchand:
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. CoRR abs/2304.02064 (2023) - 2022
- [i17]Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand:
Generalization Properties of Decision Trees on Real-valued and Categorical Features. CoRR abs/2210.10781 (2022) - 2021
- [i16]Qi Chen, Changjian Shui, Mario Marchand:
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. CoRR abs/2109.14595 (2021) - [i15]Gabriel Laberge, Yann Pequignot, Foutse Khomh, Mario Marchand, Alexandre Mathieu:
Partial order: Finding Consensus among Uncertain Feature Attributions. CoRR abs/2110.13369 (2021) - [i14]Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand:
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion. CoRR abs/2111.00062 (2021) - 2020
- [i13]Jean-Samuel Leboeuf, Frédéric Leblanc, Mario Marchand:
Decision trees as partitioning machines to characterize their generalization properties. CoRR abs/2010.07374 (2020) - 2019
- [i12]Prudencio Tossou, Basile Dura, François Laviolette, Mario Marchand, Alexandre Lacoste:
Adaptive Deep Kernel Learning. CoRR abs/1905.12131 (2019) - 2016
- [i11]Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette:
Large scale modeling of antimicrobial resistance with interpretable classifiers. CoRR abs/1612.01030 (2016) - 2015
- [i10]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 abs/1503.08329 (2015) - [i9]Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil:
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance. CoRR abs/1505.06249 (2015) - [i8]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. CoRR abs/1505.07818 (2015) - [i7]Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Émile Robitaille, Jean-Francis Roy:
Efficient Learning of Ensembles with QuadBoost. CoRR abs/1506.02535 (2015) - 2014
- [i6]Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand:
Sequential Model-Based Ensemble Optimization. CoRR abs/1402.0796 (2014) - [i5]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 abs/1412.1074 (2014) - [i4]Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand:
On the String Kernel Pre-Image Problem with Applications in Drug Discovery. CoRR abs/1412.1463 (2014) - [i3]Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand:
Domain-Adversarial Neural Networks. CoRR abs/1412.4446 (2014) - 2012
- [i2]Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil:
Learning a peptide-protein binding affinity predictor with kernel ridge regression. CoRR abs/1207.7253 (2012) - 2010
- [i1]Mohak Shah, Mario Marchand, Jacques Corbeil:
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data. CoRR abs/1005.0530 (2010)
Coauthor Index
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