M. Marchand

发表

A Growth Algorithm for Neural Network Decision Trees

M. Golea, M. Marchand, 1990 .

Domain-Adversarial Training of Neural Networks pdf

François Laviolette, Hugo Larochelle, Mario Marchand, 2015, J. Mach. Learn. Res..

A Convergence Theorem for Sequential Learning in Two-Layer Perceptrons

M. Golea, M. Marchand, P. Rujan, 1990 .

PAC-Bayesian learning of linear classifiers

François Laviolette, Mario Marchand, Pascal Germain, 2009, ICML '09.

Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets

François Laviolette, Alexandre Lacoste, Mario Marchand, 2012, AISTATS.

An Approximation Algorithm to Find the Largest Linearly Separable Subset of Training Examples

M. Golea, M. Marchand, 1993 .

The Set Covering Machine

John Shawe-Taylor, Mario Marchand, J. Shawe-Taylor, 2003, J. Mach. Learn. Res..

MHC-NP: predicting peptides naturally processed by the MHC.

J. Corbeil, M. Marchand, François Laviolette, 2013, Journal of immunological methods.

Learning curves of the clipped Hebb rule for networks with binary weights

M. Golea, M. Marchand, 1993 .

On learning simple deterministic and probabilistic neural concepts

Mario Marchand, Mostefa Golea, M. Golea, 1993, EuroCOLT.

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

François Laviolette, Mike Tyers, Mario Marchand, 2016, BMC Genomics.

Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS.

Jody C. May, Pier-Luc Plante, J. Corbeil, 2019, Analytical chemistry.

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Maxime Déraspe, J. Corbeil, M. Marchand, 2016, BMC Genomics.

MODELLING BIOLOGICAL ASSAYS WITH ADAPTIVE DEEP KERNEL LEARNING

François Laviolette, Alexandre Lacoste, Mario Marchand, 2019 .

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

M. Marchand, F. Leblanc, Jean-Samuel Leboeuf, 2022, ArXiv.

Adaptive Deep Kernel Learning pdf

Alexandre Lacoste, Mario Marchand, Prudencio Tossou, 2019, ArXiv.

Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries

Mario Marchand, Thomas R. Hancock, Mostefa Golea, 1994, Machine Learning.

On learning ?-perceptron networks on the uniform distribution

Mario Marchand, Thomas R. Hancock, Mostefa Golea, 1996, Neural Networks.

Interpretable genotype-to-phenotype classifiers with performance guarantees

F. Raymond, J. Corbeil, M. Marchand, 2018, bioRxiv.

From PAC-Bayes Bounds to Quadratic Programs for Majority Votes

François Laviolette, Mario Marchand, Jean-Francis Roy, 2011, ICML.

Domain-Adversarial Neural Networks pdf

François Laviolette, Hugo Larochelle, Mario Marchand, 2014, ArXiv.

HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels

François Laviolette, Mario Marchand, Jacques Corbeil, 2008, Retrovirology.

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

François Laviolette, Mario Marchand, Alexandre Drouin, 2015, ArXiv.

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

François Laviolette, Mario Marchand, Alexandre Drouin, 2014, ArXiv.

Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery

François Laviolette, Mario Marchand, Jacques Corbeil, 2015, PLoS Comput. Biol..

Selective Sampling for Classification

François Laviolette, Mario Marchand, Sara Shanian, 2008, Canadian Conference on AI.

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

François Laviolette, Mario Marchand, Mohak Shah, 2009, Machine Learning.

Mass spectra alignment using virtual lock-masses

Dominic Gagnon, François Laviolette, Mario Marchand, 2018, bioRxiv.

Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation

Denis Laurendeau, Mario Marchand, Sébastien Quirion, 2008, J. WSCG.

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

Mario Marchand, Mohak Shah, Jacques Corbeil, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

J. Corbeil, M. Marchand, François Laviolette, 2012, BMC Bioinformatics.

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

Mario Marchand, Changjian Shui, Qi Chen, 2021, NeurIPS.

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

M. Marchand, F. Khomh, Yann Pequignot, 2021, 2110.13369.

Partial order: Finding Consensus among Uncertain Feature Attributions

M. Marchand, F. Khomh, Yann Pequignot, 2021, arXiv.org.

Decision trees as partitioning machines to characterize their generalization properties pdf

Mario Marchand, Jean-Samuel Leboeuf, Fr'ed'eric LeBlanc, 2020, NeurIPS.

Improved design and screening of high bioactivity peptides for drug discovery

Mario Marchand, Jacques Corbeil, Franccois Laviolette, 2013, 1311.3573.

Large scale modeling of antimicrobial resistance with interpretable classifiers pdf

François Laviolette, Mario Marchand, Alexandre Drouin, 2016, ArXiv.

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

François Laviolette, Mario Marchand, Pascal Germain, 2015, J. Mach. Learn. Res..

A geometric approach to learning in neural networks

P. Rujan, M. Marchand, M. Marchand, 1989, International 1989 Joint Conference on Neural Networks.

Learning nonoverlapping perceptron networks from examples and membership queries

Mario Marchand, Thomas R. Hancock, Mostefa Golea, 2004, Machine Learning.

POLYNOMIAL TIME ALGORITHMS FOR LEARNING NEURAL NETS OF NONOVERLAPPING PERCEPTRONS

Mario Marchand, Mostefa Golea, M. Golea, 1993, Comput. Intell..

On learning simple neural concepts: from halfspace intersections to neural decision lists

Mario Marchand, Mostefa Golea, M. Golea, 1993 .

Learning with Decision Lists of Data-Dependent Features

Mario Marchand, Marina Sokolova, M. Marchand, 2005, J. Mach. Learn. Res..

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

François Laviolette, Mario Marchand, M. Marchand, 2007, J. Mach. Learn. Res..

Learning with Randomized Majority Votes

François Laviolette, Mario Marchand, Alexandre Lacasse, 2010, ECML/PKDD.

PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier

François Laviolette, Mario Marchand, Nicolas Usunier, 2006, NIPS.

A PAC-Bayes Sample-compression Approach to Kernel Methods

François Laviolette, Alexandre Lacoste, Mario Marchand, 2011, ICML.

Domain

H. Larochelle, V. Lempitsky, M. Marchand, 2019, Definitions.

Interpretable genotype-to-phenotype classifiers with performance guarantees

François Laviolette, Mario Marchand, Alexandre Drouin, 2018, Scientific Reports.

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

François Laviolette, Mario Marchand, Amélie Rolland, 2015, ICML.

Efficient Learning of Ensembles with QuadBoost pdf

François Laviolette, Mario Marchand, Jean-Francis Roy, 2015, ArXiv.

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

Mario Marchand, Jean-Samuel Leboeuf, Fr'ed'eric LeBlanc, 2021, ArXiv.

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

Juho Rousu, John Shawe-Taylor, Mario Marchand, 2014, NIPS.

MHC-NP: predicting peptides naturally processed by the MHC.

J. Corbeil, M. Marchand, François Laviolette, 2013, Journal of immunological methods.

Partial order: Finding Consensus among Uncertain Feature Attributions pdf

Foutse Khomh, Mario Marchand, Yann Pequignot, 2021, ArXiv.

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

François Laviolette, Mario Marchand, Jean-Francis Roy, 2016, AISTATS.

The Set Covering Machine with Data-Dependent Half-Spaces

John Shawe-Taylor, Mario Marchand, Marina Sokolova, 2003, ICML.

Learning with the Set Covering Machine

John Shawe-Taylor, Mario Marchand, J. Shawe-Taylor, 2001, ICML.

The Decision List Machine

John Shawe-Taylor, Mario Marchand, Nathalie Japkowicz, 2002, NIPS.

Learning by Minimizing Resources in Neural Networks

Mario Marchand, Pal Rujan, M. Marchand, 1989, Complex Syst..