Mario Marchand

Orcid: 0000-0002-7078-7393

Affiliations:
  • Université Laval


According to our database1, Mario Marchand authored at least 59 papers between 1989 and 2023.

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Bibliography

2023
On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm.
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.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Generalization Properties of Decision Trees on Real-valued and Categorical Features.
CoRR, 2022

2021
Improving Generalization Bounds for VC Classes Using the Hypergeometric Tail Inversion.
CoRR, 2021

Partial order: Finding Consensus among Uncertain Feature Attributions.
CoRR, 2021

Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis.
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.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Adaptive Deep Kernel Learning.
CoRR, 2019

2017
Domain-Adversarial Training of Neural Networks.
Proceedings of the Domain Adaptation in Computer Vision Applications., 2017

2016
Domain-Adversarial Training of Neural Networks.
J. Mach. Learn. Res., 2016

Large scale modeling of antimicrobial resistance with interpretable classifiers.
CoRR, 2016

A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery.
PLoS Comput. Biol., 2015

Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm.
J. Mach. Learn. Res., 2015

Efficient Learning of Ensembles with QuadBoost.
CoRR, 2015

Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance.
CoRR, 2015

Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
On the String Kernel Pre-Image Problem with Applications in Drug Discovery.
CoRR, 2014

Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine.
CoRR, 2014

Domain-Adversarial Neural Networks.
CoRR, 2014

Sequential Model-Based Ensemble Optimization.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Agnostic Bayesian Learning of Ensembles.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Learning a peptide-protein binding affinity predictor with kernel ridge regression.
BMC Bioinform., 2013

Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data.
IEEE Trans. Pattern Anal. Mach. Intell., 2012

Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

2011
From PAC-Bayes Bounds to Quadratic Programs for Majority Votes.
Proceedings of the 28th International Conference on Machine Learning, 2011

A PAC-Bayes Sample-compression Approach to Kernel Methods.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Learning the set covering machine by bound minimization and margin-sparsity trade-off.
Mach. Learn., 2010

Learning with Randomized Majority Votes.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

2009
From PAC-Bayes Bounds to KL Regularization.
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.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation.
J. WSCG, 2008

Selective Sampling for Classification.
Proceedings of the Advances in Artificial Intelligence , 2008

2007
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers.
J. Mach. Learn. Res., 2007

Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data.
J. Mach. Learn. Res., 2007

2006
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

A PAC-Bayes Risk Bound for General Loss Functions.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005
Learning with Decision Lists of Data-Dependent Features.
J. Mach. Learn. Res., 2005

A PAC-Bayes approach to the Set Covering Machine.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

PAC-Bayes risk bounds for sample-compressed Gibbs classifiers.
Proceedings of the Machine Learning, 2005

Margin-Sparsity Trade-Off for the Set Covering Machine.
Proceedings of the Machine Learning: ECML 2005, 2005

2004
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data.
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.
Proceedings of the Machine Learning, 2003

2002
The Set Covering Machine.
J. Mach. Learn. Res., 2002

The Decision List Machine.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001
Learning with the Set Covering Machine.
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.
Neural Networks, 1996

1995
Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994
Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries.
Mach. Learn., 1994

Learning Stochastic Perceptrons Under k-Blocking Distributions.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

1993
On Learning Perceptrons with Binary Weights.
Neural Comput., 1993

Polynomial Time Algorithms for Learning Neural Nets of NonoverlappingPerceptrons.
Comput. Intell., 1993

On learning simple deterministic and probabilistic neural concepts.
Proceedings of the First European Conference on Computational Learning Theory, 1993

Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks.
Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, 1993

1992
On Learning µ-Perceptron Networks with Binary Weights.
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.
Complex Syst., 1989


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