Learning by Minimizing Resources in Neural Networks. - Pal Rujan; Mario Marchand (Complex Syst., 1989)

On Learning µ-Perceptron Networks with Binary Weights. - Mostefa Golea; Mario Marchand; Thomas R. Hancock (NIPS, 1992)

Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks. - Mostefa Golea; Mario Marchand (COLT, 1993)

On learning simple deterministic and probabilistic neural concepts. - Mostefa Golea; Mario Marchand (EuroCOLT, 1993)

Polynomial Time Algorithms for Learning Neural Nets of NonoverlappingPerceptrons. - Mostefa Golea; Mario Marchand (Comput. Intell., 1993)

On Learning Perceptrons with Binary Weights. - Mostefa Golea; Mario Marchand (Neural Comput., 1993)

Learning Stochastic Perceptrons Under k-Blocking Distributions. - Mario Marchand; Saeed Hadjifaradji (NIPS, 1994)

Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries. - Thomas R. Hancock; Mostefa Golea; Mario Marchand (Mach. Learn., 1994)

Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks. - Mario Marchand; Saeed Hadjifaradji (NIPS, 1995)

On learning ?-perceptron networks on the uniform distribution. - Mostefa Golea; Mario Marchand; Thomas R. Hancock (Neural Networks, 1996)

Learning with the Set Covering Machine. - Mario Marchand; John Shawe-Taylor (ICML, 2001)

The Decision List Machine. - Marina Sokolova; Mario Marchand; Nathalie Japkowicz; John Shawe-Taylor (NIPS, 2002)

The Set Covering Machine. - Mario Marchand; John Shawe-Taylor (J. Mach. Learn. Res., 2002)

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

PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data. - Mario Marchand; Mohak Shah (NIPS, 2004)

Margin-Sparsity Trade-Off for the Set Covering Machine. - François Laviolette; Mario Marchand; Mohak Shah (ECML, 2005)

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. - François Laviolette; Mario Marchand (ICML, 2005)

A PAC-Bayes approach to the Set Covering Machine. - François Laviolette; Mario Marchand; Mohak Shah (NIPS, 2005)

Learning with Decision Lists of Data-Dependent Features. - Mario Marchand; Marina Sokolova (J. Mach. Learn. Res., 2005)

A PAC-Bayes Risk Bound for General Loss Functions. - Pascal Germain; Alexandre Lacasse; François Laviolette; Mario Marchand (NIPS, 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 (NIPS, 2006)

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)

PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. - François Laviolette; Mario Marchand (J. Mach. Learn. Res., 2007)

Selective Sampling for Classification. - François Laviolette; Mario Marchand; Sara Shanian (Canadian AI, 2008)

Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation. - Sébastien Quirion; Chantale Duchesne; Denis Laurendeau; Mario Marchand (J. WSCG, 2008)

PAC-Bayesian learning of linear classifiers. - Pascal Germain; Alexandre Lacasse; François Laviolette; Mario Marchand (ICML, 2009)

From PAC-Bayes Bounds to KL Regularization. - Pascal Germain; Alexandre Lacasse; François Laviolette; Mario Marchand; Sara Shanian (NIPS, 2009)

Learning with Randomized Majority Votes. - Alexandre Lacasse; François Laviolette; Mario Marchand; Francis Turgeon-Boutin (ECML/PKDD (2), 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)

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 (ICML, 2011)

Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets. - Alexandre Lacoste; François Laviolette; Mario Marchand (AISTATS, 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)

Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. - Sébastien Giguère; François Laviolette; Mario Marchand; Khadidja Sylla (ICML (1), 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)

Agnostic Bayesian Learning of Ensembles. - Alexandre Lacoste; Mario Marchand; François Laviolette; Hugo Larochelle (ICML, 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 (NIPS, 2014)

Sequential Model-Based Ensemble Optimization. - Alexandre Lacoste; Hugo Larochelle; Mario Marchand; François Laviolette (UAI, 2014)

Domain-Adversarial Neural Networks. - Hana Ajakan; Pascal Germain; Hugo Larochelle; 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)

On the String Kernel Pre-Image Problem with Applications in Drug Discovery. - Sébastien Giguère; Amélie Rolland; François Laviolette; Mario Marchand (CoRR, 2014)

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 (ICML, 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)

Efficient Learning of Ensembles with QuadBoost. - Louis Fortier-Dubois; François Laviolette; Mario Marchand; Louis-Émile Robitaille; Jean-Francis Roy (CoRR, 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)

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)

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

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)

Domain-Adversarial Training of Neural Networks. - Yaroslav Ganin; Evgeniya Ustinova; Hana Ajakan; Pascal Germain; Hugo Larochelle; François Laviolette; Mario Marchand; Victor S. Lempitsky (Domain Adaptation in Computer Vision Applications, 2017)

Adaptive Deep Kernel Learning. - Prudencio Tossou; Basile Dura; François Laviolette; Mario Marchand; Alexandre Lacoste (CoRR, 2019)

Decision trees as partitioning machines to characterize their generalization properties. - Jean-Samuel Leboeuf; Frédéric Leblanc; Mario Marchand (NeurIPS, 2020)

Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis. - Qi Chen 015; Changjian Shui; Mario Marchand (NeurIPS, 2021)

Partial order: Finding Consensus among Uncertain Feature Attributions. - Gabriel Laberge; Yann Pequignot; Foutse Khomh; Mario Marchand; Alexandre Mathieu (CoRR, 2021)

Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion. - Jean-Samuel Leboeuf; Frédéric Leblanc; Mario Marchand (CoRR, 2021)

Generalization Properties of Decision Trees on Real-valued and Categorical Features. - Jean-Samuel Leboeuf; Frédéric Leblanc; Mario Marchand (CoRR, 2022)

Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. - Qi Chen 015; Mario Marchand (AISTATS, 2023)

Fooling SHAP with Stealthily Biased Sampling. - Gabriel Laberge; Ulrich Aïvodji; Satoshi Hara 001; Mario Marchand; Foutse Khomh (ICLR, 2023)

On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm. - Qi Chen 015; Changjian Shui; Ligong Han; Mario Marchand (NeurIPS, 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)

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

Tighter Risk Bounds for Mixtures of Experts. - Wissam Akretche; Frédéric Leblanc; Mario Marchand (CoRR, 2024)

Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions. - Gabriel Dubé; Mario Marchand (Trans. Mach. Learn. Res., 2025)