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Sébastien Giguère, Alexandre Drouin,
Alexandre Lacoste, Mario Marchand, Jacques Corbeil, François Laviolette.
MHC-NP : Predicting
Peptides Naturally Processed by the MHC. Journal of Immunological
Methods, vol 400, pp. 30---36, (2013) Sébastien
Giguère, François Laviolette, Mario Marchand, and Khadidja Sylla. Risk
bounds and learning algorithms for the regression approach to structured
output prediction, Proceedings of the 30th International Conference
on Machine Learning, Atlanta, Georgia 2013. Journal of Machine
Learning Research W&CP, vol. 28, (2013). Sébastien
Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, and
Jacques Corbeil. Learning
a peptide-protein binding affinity predictor with kernel ridge regression,
BMC Bioinformatics, vol. 14 (1), pp. 82—97, (2013). Alexandre
Lacoste, Francois Laviolette, Mario Marchand. Bayesian Comparison of
Machine Learning Algorithms on Single and Multiple Datasets, JMLR
W&CP 22: 665-675, 2012 François
Laviolette, Mario Marchand, and Jean-Francis Roy. From PAC-Bayes Bounds
to Quadratic Programs for Majority Votes. In Proceedings of the 28th International Conference on Machine
Learning, Bellevue, WA, USA, June 2011. [ bib |
.pdf | Extended
Version | Source
Code ] Pascal Germain, Alexandre Lacoste, François Laviolette, Mario
Marchand, and Sara Shanian. A PAC-Bayes Sample Compression Approach to
Kernel Methods. In Proceedings of the
28th International Conference on Machine Learning, Bellevue,
WA, USA, June 2011. [ bib |
.pdf | Supplementary
Material | Source
Code ] 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). For the preliminary version see also: http://arxiv.org/abs/1005.0530 . A. Lacasse, F. Laviolette, M. Marchand, and
F. Turgeon-Boutin. Learning with Randomized Majority Votes. Springer LNCS vol. 6322, pages 162-177, (2010).
[ bib |
.pdf ]
François Laviolette, Mario Marchand, Mohak Shah,
and Sara Shanian. Learning
the set covering machine by bound minimization
and margin-sparsity trade-off. Machine
Learning, Vol. 78, pp. 175—201 (2010). Pascal
Germain, Alexandre Lacasse, Francois Laviolette, Mario Marchand and Sara
Shanian. From
PAC-Bayes Bounds to KL Regularization, Advances in Neural Information
Processing Systems 22, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I.
Williams, A. Culotta editors, pp. 603—610 (2009). Pascal Germain, Alexandre Lacasse, François
Laviolette, and Mario Marchand. PAC-Bayesian
Learning of Linear Classifiers. Proceedings
of the 26th International Conference on Machine Learning (ICML 2009),
(2009). Sébastien Boisvert, Mario Marchand, François
Laviolette, and Jacques Corbeil, HIV-1 coreceptor usage prediction without multiple alignments: an
application of string kernels. Retrovirology,
doi:10.1186/1742-4690-5-110, vol 5:11, 14 pages (2008). François
Laviolette, Mario Marchand, and Sara Shanian. Selective Sampling for Classification. Advances in Artificial
Intelligence, Springer LNAI vol. 5032, pp.
191--202 (2008). Sébastien Quirion, Chantale Duchesne, Denis
Laurendeau and Mario Marchand, Comparing
GPLVM Approaches for Dimensionality Reduction in Character Animation. Journal
of WSCG, vol. 16, no 1-3, pp. 41-48, 2008. Zakria Hussain, François Laviolette and Mario
Marchand, John Shawe-Taylor, Spencer 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,
vol. 8, pp. 2533--2549 (2007). François
Laviolette and Mario Marchand. PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes
of Sample-Compressed Classifiers, Journal of Machine Learning Research, vol. 8, pp. 1461--1487
(2007). Pascal
Germain, Alexandre Lacasse, François Laviolette, and Mario Marchand. PAC-Bayes Risk Bounds for
General Loss Functions, Advances in Neural Information Processing
Systems 19 (Proceedings of NIPS 2006), B. Schölkopf, J. Platt, and T.
Hoffman editors, MIT-Press, pp. 449--456, Cambridge MA (2007). Alexandre
Lacasse, François Laviolette, Mario Marchand, Pascal Germain and Nicolas
Usunier. PAC-Bayes Bounds for the Risk
of the Majority Vote and the Variance of the Gibbs Classifier, Advances in Neural
Information Processing Systems 19 (Proceedings of NIPS 2006), B.
Schölkopf, J. Platt, and T. Hoffman editors, MIT-Press, pp. 769--776,
Cambridge MA (2007). François Laviolette, Mario Marchand and Mohak
Shah. A PAC-Bayes Approach to the Set Covering Machine. Advances
in Neural Information Processing Systems 18 (Proceedings of
NIPS 2005). pp. 731--738, MIT-Press
(2006). François Laviolette, Mario Marchand, and Mohak
Shah. Margin-Sparsity
Trade-off for the Set Covering Machine. Proceedings of the 16th
European Conference on Machine Learning (ECML'05). Springer LNAI
vol. 3720, pp. 206--217 (2005). François Laviolette and Mario Marchand. PAC-Bayes
Risk Bounds for Sample-Compressed Gibbs Classifiers. Proceedings of the Twenty
Second International Conference on Machine Learning (ICML'05), pp.
481--488, ACM Press (2005). Mario Marchand and Marina Sokolova. Learning with Decision Lists of Data-Dependent Features, Journal of Machine Learning Research, vol. 6, pp.
427-451 (2005). Mario Marchand and Mohak Shah. PAC-Bayes Learning of Conjunctions and
Classification of Gene-Expression Data, Advances in Neural Information Processing
Systems 17 (Proceedings of NIPS 2004), pp. 881-888, MIT-Press,
Cambridge, MA, (2005). Mario Marchand, Mohak Shah, John Shawe-Taylor,
and Marina Sokolova The
Set Covering Machine with Data-Dependent Half-Spaces, Proceedings of the
Twentieth International Conference on Machine Learning (ICML'2003),
pp. 520--527, AAAI Press, Menlo Park CA, (2003). Marina Sokolova, Mario Marchand, Nathalie
Japkowicz, and John Shawe-Taylor, The
Decision List Machine, Advances in Neural Information Processing
Systems 15, pp. 921--928, MIT-Press, Cambridge, MA, USA,
(2003). Mario Marchand and John Shawe-Taylor, The Set Covering Machine, Journal of Machine Learning Research,
vol. 3, pp. 723-746 (2002). Mario Marchand and John Shawe-Taylor, Learning with the Set Covering Machine, Proceedings of the
Eighteenth International Conference on Machine Learning (ICML'2001),
pp. 345--352, Morgan Kaufmann, San Francisco CA, (2001). Pal Rujan and Mario Marchand, Computing the Bayes Kernel Classifier, in A. J. Smola, P. L.
Bartlett, B. Schoelkopf, D. E. Schuurmans eds., Advances in Large
Margin Classifiers, pp. 329--347, MIT Press, Cambridge MA (2000). Mario Marchand and Saeed Hadjifaradji, Strong Unimodality and Exact Learning of
mu-Perceptron Networks in D. S. Touretzky, M. C.
Mozer, M. E. Hasselmo, eds., Advances in Neural Information
Processing Systems 8, pp. 288--294, MIT Press, Cambridge MA, (1996). Mostefa Golea, Mario Marchand and Thomas
Hancock, On Learning mu-Perceptron Networks On the
Uniform Distribution, Neural Networks, vol. 9, pp. 67--82 (1996). Mario Marchand and Saeed Hadjifaradji, Learning Stochastic Perceptrons under
k-Blocking Distribution, in G. Tesauro, D. S. Touretzky and T. K. Leen,
eds., Advances in Neural Information Processing Systems 7, pp.
279--286, MIT Press, Cambridge MA, (1995). Thomas Hancock, Mostefa Golea and Mario
Marchand. Learning Nonoverlapping Perceptron Networks from Examples and
Membership Queries, Machine Learning, vol. 16, pp. 161--183 (1994). Mostefa Golea and Mario Marchand, On Learning Simple Deterministic and Probabilistic Neural
Concepts, in Shawe-Talor John , Anthony Martin,
eds., Computational Learning Theory: EuroCOLT'93, pp. 47--60,
Oxford University Press, (1994). Mostefa Golea and Mario Marchand, Average Case Analysis of the Clipped Hebb
Rule for Nonoverlapping Perceptron Networks, Proceedings of the Sixth
Annual ACM Conference on Computational Learning Theory, pp. 151--157, ACM press,
(1993). Mario Marchand and Mostefa Golea, An Approximation Algorithm to Find the
Largest Linearly Separable Subset of Training Examples, World Congress on Neural
Networks'93: Proceedings of the 1993 Annual Meeting of the International
Neural Network Society, vol. 3, pp. 556--559, Hillsdale, NJ: Erlbaum Associates, (1993). Mario Marchand and Mostefa Golea, A Constructive Algorithm for Neural
Decision Lists, World Congress on Neural Networks'93:
Proceedings of the 1993 Annual Meeting of the International Neural
Network Society, vol. 3, pp. 560--563, Hillsdale, NJ: Erlbaum Associates, (1993). Mostefa Golea, Mario Marchand and Thomas
Hancock, On Learning mu-Perceptron Networks with
Binary Weights, in Giles C.L., Hanson S.J. and Cowan J.D. (eds.), Advances in
Neural Information Processing Systems~5, pp. 591--598, San Mateo CA,
Morgan Kaufmann Publishers, (1993). Mostefa Golea and Mario Marchand, Learning Curves of the Clipped Hebb Rule for Networks with Binary
Weights, Journal of Physics A, vol. 26, pp 5751--5766 (1993). Mostefa Golea and Mario Marchand, On Learning Perceptrons with
Binary Weights, Neural Computation, vol. 5, pp. 767--782 (1993). Mario Marchand and Mostefa Golea. On Learning Simple Neural Concepts: from Halfspace Intersections
to Neural Decision Lists, Network: Computation in Neural Systems, vol. 4, pp. 67--85 (1993) Mostefa Golea and Mario Marchand, Polynomial
Time Algorithms for Learning Neural Nets of Nonoverlapping Perceptrons, Computational
Intelligence, vol. 9, pp. 155--170 (1993). Mostefa Golea and Mario Marchand, A Growth Algorithm for Neural Network
Decision Trees, Europhysics Letters, vol. 12, pp. 205--210 (1990). Mario
Marchand, Mostefa Golea and Pal Rujan, A Convergence Theorem for
Sequential Learning in Two Layer Perceptrons, Europhysics Letters, vol. 11, pp. 487--492
(1990). Pal
Rujan and Mario Marchand. A Geometric Approach to
Learning in Neural Networks,
Proceedings of the International Joint Conference on Neural Networks II,
IEEE TAB Neural Network Committee, pp. 105--109 (1989). Pal
Rujan and Mario Marchand. Learning by Minimizing
Resources in Neural Network, Complex
Systems, vol. 3, pp 229--242 (1989). |