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Mario Marchand
2010 – today
- 2013
[j13]Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil: Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC Bioinformatics 14: 82 (2013)- 2012
- [j12]Alexandre Lacoste, François Laviolette, Mario Marchand: Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets. Journal of Machine Learning Research - Proceedings Track 22: 665-675 (2012)
- [j11]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)
- [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)
- 2011
- [c19]Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian: A PAC-Bayes Sample-compression Approach to Kernel Methods. ICML 2011: 297-304
- [c18]Jean-Francis Roy, François Laviolette, Mario Marchand: From PAC-Bayes Bounds to Quadratic Programs for Majority Votes. ICML 2011: 649-656
- 2010
- [j10]François Laviolette, Mario Marchand, Mohak Shah, Sara Shanian: Learning the set covering machine by bound minimization and margin-sparsity trade-off. Machine Learning 78(1-2): 175-201 (2010)
- [c17]Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin: Learning with Randomized Majority Votes. ECML/PKDD (2) 2010: 162-177
- [i1]Mohak Shah, Mario Marchand, Jacques Corbeil: Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data. CoRR abs/1005.0530 (2010)
2000 – 2009
- 2009
- [c16]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand: PAC-Bayesian learning of linear classifiers. ICML 2009: 45
- [c15]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian: From PAC-Bayes Bounds to KL Regularization. NIPS 2009: 603-610
- 2008
- [j9]Sébastien Quirion, Chantale Duchesne, Denis Laurendeau, Mario Marchand: Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation. Journal of WSCG 16(1-3): 41-48 (2008)
- [c14]François Laviolette, Mario Marchand, Sara Shanian: Selective Sampling for Classification. Canadian Conference on AI 2008: 191-202
- 2007
- [j8]François Laviolette, Mario Marchand: PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. Journal of Machine Learning Research 8: 1461-1487 (2007)
- [j7]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. Journal of Machine Learning Research 8: 2533-2549 (2007)
- 2006
- [c13]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand: A PAC-Bayes Risk Bound for General Loss Functions. NIPS 2006: 449-456
- [c12]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
- [e1]Luc Lamontagne, Mario Marchand (Eds.): 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
- 2005
- [j6]Mario Marchand, Marina Sokolova: Learning with Decision Lists of Data-Dependent Features. Journal of Machine Learning Research 6: 427-451 (2005)
- [c11]François Laviolette, Mario Marchand, Mohak Shah: Margin-Sparsity Trade-Off for the Set Covering Machine. ECML 2005: 206-217
- [c10]François Laviolette, Mario Marchand: PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. ICML 2005: 481-488
- [c9]François Laviolette, Mario Marchand, Mohak Shah: A PAC-Bayes approach to the Set Covering Machine. NIPS 2005
- 2004
- [c8]Mario Marchand, Mohak Shah: PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data. NIPS 2004
- 2003
- [c7]Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova: The Set Covering Machine with Data-Dependent Half-Spaces. ICML 2003: 520-527
- 2002
- [j5]Mario Marchand, John Shawe-Taylor: The Set Covering Machine. Journal of Machine Learning Research 3: 723-746 (2002)
- [c6]Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor: The Decision List Machine. NIPS 2002: 921-928
- 2001
- [c5]
1990 – 1999
- 1996
- [j4]Mostefa Golea, Mario Marchand, Thomas R. Hancock: On learning ?-perceptron networks on the uniform distribution. Neural Networks 9(1): 67-82 (1996)
- 1995
- [c4]Mario Marchand, Saeed Hadjifaradji: Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks. NIPS 1995: 288-294
- 1994
- [j3]Thomas R. Hancock, Mostefa Golea, Mario Marchand: Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries. Machine Learning 16(3): 161-183 (1994)
- [c3]Mario Marchand, Saeed Hadjifaradji: Learning Stochastic Perceptrons Under k-Blocking Distributions. NIPS 1994: 279-286
- 1993
- [j2]Mostefa Golea, Mario Marchand: Polynomial Time Algorithms for Learning Neural Nets of NonoverlappingPerceptrons. Computational Intelligence 9: 155-170 (1993)
- [j1]Mostefa Golea, Mario Marchand: On Learning Perceptrons with Binary Weights. Neural Computation 5(5): 767-782 (1993)
- [c2]Mostefa Golea, Mario Marchand: Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks. COLT 1993: 151-157
- 1992
- [c1]Mostefa Golea, Mario Marchand, Thomas R. Hancock: On Learning µ-Perceptron Networks with Binary Weights. NIPS 1992: 591-598
Coauthor Index
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last updated on 2013-03-15 18:02 CET by the dblp team