Mario Marchand

发表

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

Mario Marchand, Mohak Shah, 2004, NIPS.

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

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

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.

The Set Covering Machine with Data-Dependent Half-Spaces

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

From PAC-Bayes Bounds to KL Regularization

François Laviolette, Mario Marchand, Pascal Germain, 2009, NIPS.

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

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

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

Mario Marchand, Saeed Hadjifaradji, 1995, NIPS.

Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets

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

Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data

John Shawe-Taylor, François Laviolette, Mario Marchand, 2007, J. Mach. Learn. Res..

Average case analysis of the clipped Hebb rule for nonoverlapping perception networks

Mario Marchand, Mostefa Golea, 1993, COLT '93.

Interpretable genotype-to-phenotype classifiers with performance guarantees

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

Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery

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

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers

François Laviolette, Mario Marchand, 2005, ICML '05.

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

Luc Lamontagne, Mario Marchand, 2006, Canadian Conference on AI.

A PAC-Bayes approach to the Set Covering Machine

François Laviolette, Mario Marchand, Mohak Shah, 2005, NIPS.

On Learning µ-Perceptron Networks with Binary Weights

Mario Marchand, Thomas R. Hancock, Mostefa Golea, 1992, NIPS.

Learning with Randomized Majority Votes

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

Computing the Bayes Kernel Classifler

Mario Marchand, Pal Rujan, Carl von Ossietzky, 2004 .

Agnostic Bayesian Learning of Ensembles

François Laviolette, Hugo Larochelle, Alexandre Lacoste, 2014, ICML.

Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation

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

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

François Laviolette, Mario Marchand, Amélie Rolland, 2014, ArXiv.

Large scale modeling of antimicrobial resistance with interpretable classifiers pdf

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

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

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

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

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

On learning ?-perceptron networks on the uniform distribution

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

POLYNOMIAL TIME ALGORITHMS FOR LEARNING NEURAL NETS OF NONOVERLAPPING PERCEPTRONS

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

Sequential Model-Based Ensemble Optimization pdf

François Laviolette, Hugo Larochelle, Alexandre Lacoste, 2014, UAI.

MODELLING BIOLOGICAL ASSAYS WITH ADAPTIVE DEEP KERNEL LEARNING

François Laviolette, Alexandre Lacoste, Mario Marchand, 2019 .

Mass spectra alignment using virtual lock-masses

François Laviolette, Mario Marchand, Alexandre Drouin, 2018 .

Adaptive Deep Kernel Learning pdf

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

Mass spectra alignment using virtual lock-masses

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

Learning by Minimizing Resources in Neural Networks

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

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

François Laviolette, Mario Marchand, Alexandre Drouin, 2012, BMC Bioinformatics.

Margin-Sparsity Trade-Off for the Set Covering Machine

François Laviolette, Mario Marchand, Mohak Shah, 2005, ECML.

Selective Sampling for Classification

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

Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries

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

PAC-Bayesian learning of linear classifiers

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

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

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

Domain-Adversarial Neural Networks pdf

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

Learning Stochastic Perceptrons Under k-Blocking Distributions

Mario Marchand, Saeed Hadjifaradji, 1994, NIPS.

Learning with Decision Lists of Data-Dependent Features

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

Domain-Adversarial Training of Neural Networks pdf

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

On Learning Perceptrons with Binary Weights

Mario Marchand, Mostefa Golea, 1993, Neural Computation.

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.

Learning with the Set Covering Machine

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

Improved design and screening of high bioactivity peptides for drug discovery

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

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..

Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City, ... (Lecture Notes in Computer Science)

Luc Lamontagne, Mario Marchand, 2006 .

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

François Laviolette, Mario Marchand, Sébastien Giguère, 2013, ICML.

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 PAC-Bayes Sample-compression Approach to Kernel Methods

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

Learning nonoverlapping perceptron networks from examples and membership queries

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

The Set Covering Machine

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

From PAC-Bayes Bounds to Quadratic Programs for Majority Votes

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

Decision trees as partitioning machines to characterize their generalization properties pdf

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

Interpretable genotype-to-phenotype classifiers with performance guarantees

François Laviolette, Mario Marchand, Alexandre Drouin, 2018 .

The Decision List Machine

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

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

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

Efficient Learning of Ensembles with QuadBoost pdf

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

A PAC-Bayes Risk Bound for General Loss Functions

François Laviolette, Mario Marchand, Pascal Germain, 2006, NIPS.

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.

Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence

Luc Lamontagne, Mario Marchand, 2006 .

Partial order: Finding Consensus among Uncertain Feature Attributions pdf

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

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

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

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

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

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

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

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.