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ORCIDFrançois Laviolette
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- affiliation: Université Laval
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2020 – today
- 2023
[j33]Josée Desharnais
, François Laviolette, Héli Marcoux, Norbert Polat:
A cop-winning strategy on strongly cop-win graphs. Discret. Math. 346(8): 113419 (2023)- [c55]Louis Fortier-Dubois, Benjamin Leblanc, Gaël Letarte, François Laviolette, Pascal Germain:
PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations. Canadian AI 2023 - [c54]Mazid Abiodoun Osseni, Prudencio Tossou, François Laviolette, Jacques Corbeil:
MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions. BIOINFORMATICS 2023: 252-261 - 2022
- [j32]Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette:
How to certify machine learning based safety-critical systems? A systematic literature review. Autom. Softw. Eng. 29(2): 38 (2022) - [j31]Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette:
Toolbox for Multimodal Learn (scikit-multimodallearn). J. Mach. Learn. Res. 23: 51:1-51:7 (2022) - 2021
- [j30]Rogia Kpanou, Mazid Abiodoun Osseni, Prudencio Tossou, François Laviolette, Jacques Corbeil:
On the robustness of generalization of drug-drug interaction models. BMC Bioinform. 22(1): 477 (2021) - [j29]Caroline Sirois, Richard Khoury, Audrey Durand, Pierre-Luc Déziel, Olga Bukhtiyarova, Yohann Chiu, Denis Talbot, Alexandre Bureau, Philippe Després, Christian Gagné, François Laviolette, Anne-Marie Savard, Jacques Corbeil, Thierry Badard, Sonia Jean, Marc Simard:
Exploring polypharmacy with artificial intelligence: data analysis protocol. BMC Medical Informatics Decis. Mak. 21(1): 219 (2021) - [j28]Frédéric Simard, Josée Desharnais, François Laviolette:
General Cops and Robbers games with randomness. Theor. Comput. Sci. 887: 30-50 (2021) - [c53]Mazid Abiodoun Osseni, Prudencio Tossou, Jacques Corbeil, François Laviolette:
Applying PySCMGroup to Breast Cancer Biomarkers Discovery. BIOINFORMATICS 2021: 72-82 - [c52]Marouane Yassine, David Beauchemin, François Laviolette, Luc Lamontagne:
Leveraging Subword Embeddings for Multinational Address Parsing. CIST 2021: 353-360 - [i33]Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette:
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review. CoRR abs/2107.12045 (2021) - [i32]Louis Fortier-Dubois, Gaël Letarte, Benjamin Leblanc, François Laviolette, Pascal Germain:
Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations. CoRR abs/2110.15137 (2021) - [i31]Marouane Yassine, David Beauchemin, François Laviolette, Luc Lamontagne:
Multinational Address Parsing: A Zero-Shot Evaluation. CoRR abs/2112.04008 (2021) - 2020
- [j27]Ulysse Côté Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik J. Scheme, François Laviolette, Benoit Gosselin:
Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition. IEEE Access 8: 177941-177955 (2020) - [j26]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayes and domain adaptation. Neurocomputing 379: 379-397 (2020) - [j25]Baptiste Bauvin, Cécile Capponi, Jean-Francis Roy, François Laviolette:
Fast greedy C-bound minimization with guarantees. Mach. Learn. 109(9-10): 1945-1986 (2020) - [c51]Faizy Ahsan, Alexandre Drouin, François Laviolette, Doina Precup, Mathieu Blanchette:
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites. BIBM 2020: 62-66 - [c50]Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Marcel van Gerven, François Laviolette:
The Indian Chefs Process. UAI 2020: 600-608 - [i30]Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-draa, Marcel van Gerven, François Laviolette:
The Indian Chefs Process. CoRR abs/2001.10657 (2020) - [i29]Frédéric Simard, Josée Desharnais, François Laviolette:
General Cops and Robbers Games with randomness. CoRR abs/2004.11503 (2020) - [i28]Marouane Yassine, David Beauchemin, François Laviolette, Luc Lamontagne:
Leveraging Subword Embeddings for Multinational Address Parsing. CoRR abs/2006.16152 (2020) - [i27]Yann Pequignot, Mathieu Alain, Patrick Dallaire, Alireza Yeganehparast, Pascal Germain, Josée Desharnais, François Laviolette:
Implicit Variational Inference: the Parameter and the Predictor Space. CoRR abs/2010.12995 (2020)
2010 – 2019
- 2019
- [j24]Ulysse Côté Allard, Gabriel Gagnon-Turcotte, François Laviolette, Benoit Gosselin:
A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition. Sensors 19(12): 2811 (2019) - [c49]Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette:
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. NeurIPS 2019: 6869-6879 - [c48]Gildas Kouko, Josée Desharnais, François Laviolette:
Finite Approximation of LMPs for Exact Verification of Reachability Properties. QEST 2019: 70-87 - [d1]Ulysse Côté Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik J. Scheme, François Laviolette, Benoit Gosselin:
Long-term 3DC Dataset. IEEE DataPort, 2019
- [i26]Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette:
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. CoRR abs/1905.10259 (2019) - [i25]Prudencio Tossou, Basile Dura, François Laviolette, Mario Marchand, Alexandre Lacoste:
Adaptive Deep Kernel Learning. CoRR abs/1905.12131 (2019) - [i24]Ulysse Côté Allard, Evan Campbell, Angkoon Phinyomark, François Laviolette, Benoit Gosselin, Erik J. Scheme:
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features. CoRR abs/1912.00283 (2019) - [i23]Ulysse Côté Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik J. Scheme, François Laviolette, Benoit Gosselin:
Virtual Reality to Study the Gap Between Offline and Real-Time EMG-based Gesture Recognition. CoRR abs/1912.09380 (2019) - [i22]Ulysse Côté Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik J. Scheme, François Laviolette, Benoit Gosselin:
Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition. CoRR abs/1912.11037 (2019) - 2018
- [c47]Gaël Letarte, Frédérik Paradis, Philippe Giguère, François Laviolette:
Importance of Self-Attention for Sentiment Analysis. BlackboxNLP@EMNLP 2018: 267-275 - [i21]Ulysse Côté Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clément Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin:
Deep Learning for Electromyographic Hand Gesture Signal Classification by Leveraging Transfer Learning. CoRR abs/1801.07756 (2018) - 2017
- [j23]François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy:
Risk upper bounds for general ensemble methods with an application to multiclass classification. Neurocomputing 219: 15-25 (2017) - [c46]Ulysse Côté Allard, Gabriel Dube, Richard Khoury, Luc Lamontagne, Benoit Gosselin, François Laviolette:
Time Adaptive Dual Particle Swarm Optimization. CEC 2017: 2534-2543 - [c45]Alexandre Drouin, Toby Hocking, François Laviolette:
Maximum Margin Interval Trees. NIPS 2017: 4947-4956 - [c44]Ulysse Côté Allard, David St-Onge, Philippe Giguère, François Laviolette, Benoit Gosselin:
Towards the use of consumer-grade electromyographic armbands for interactive, artistic robotics performances. RO-MAN 2017: 1030-1036 - [c43]Ulysse Côté Allard, Cheikh Latyr Fall, Alexandre Campeau-Lecours, Clément Gosselin, François Laviolette, Benoit Gosselin:
Transfer learning for sEMG hand gestures recognition using convolutional neural networks. SMC 2017: 1663-1668 - [p1]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. Domain Adaptation in Computer Vision Applications 2017: 189-209 - [i20]Alexandre Drouin, Toby Dylan Hocking, François Laviolette:
Maximum Margin Interval Trees. CoRR abs/1710.04234 (2017) - 2016
- [j22]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. J. Mach. Learn. Res. 17: 59:1-59:35 (2016) - [c42]Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy:
PAC-Bayesian Bounds based on the Rényi Divergence. AISTATS 2016: 435-444 - [c41]Jean-Francis Roy, Mario Marchand, François Laviolette:
A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees. AISTATS 2016: 1241-1249 - [c40]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A New PAC-Bayesian Perspective on Domain Adaptation. ICML 2016: 859-868 - [c39]Ulysse Côté Allard, François Nougarou, Cheikh Latyr Fall, Philippe Giguère, Clément Gosselin, François Laviolette, Benoit Gosselin:
A convolutional neural network for robotic arm guidance using sEMG based frequency-features. IROS 2016: 2464-2470 - [i19]Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette:
Large scale modeling of antimicrobial resistance with interpretable classifiers. CoRR abs/1612.01030 (2016) - 2015
- [j21]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy:
Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm. J. Mach. Learn. Res. 16: 787-860 (2015) - [j20]Sébastien Giguère, François Laviolette, Mario Marchand, Denise M. Tremblay, Sylvain Moineau, Xinxia Liang, Éric Biron, Jacques Corbeil:
Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery. PLoS Comput. Biol. 11(4) (2015) - [c38]Frédéric Simard, Michael Morin, Claude-Guy Quimper, François Laviolette, Josée Desharnais:
Bounding an Optimal Search Path with a Game of Cop and Robber on Graphs. CP 2015: 403-418 - [c37]Ulysse Côté Allard, Richard Khoury, Luc Lamontagne, Jonathan Bergeron, François Laviolette, Alexandre Bergeron Guyard:
Optimizing Question-Answering Systems Using Genetic Algorithms. FLAIRS 2015: 32-37 - [c36]Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand:
Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction. ICML 2015: 2021-2029 - [c35]Michael Morin, Frédérik Paradis, Amélie Rolland, Jean Wery, Jonathan Gaudreault, François Laviolette:
Machine learning-based metamodels for sawing simulation. WSC 2015: 2160-2171 - [i18]François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy:
On Generalizing the C-Bound to the Multiclass and Multi-label Settings. CoRR abs/1501.03001 (2015) - [i17]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. CoRR abs/1501.03002 (2015) - [i16]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. CoRR abs/1503.06944 (2015) - [i15]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy:
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. CoRR abs/1503.08329 (2015) - [i14]Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil:
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance. CoRR abs/1505.06249 (2015) - [i13]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. CoRR abs/1505.07818 (2015) - [i12]Louis Fortier-Dubois, François Laviolette, Mario Marchand, Louis-Émile Robitaille, Jean-Francis Roy:
Efficient Learning of Ensembles with QuadBoost. CoRR abs/1506.02535 (2015) - [i11]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A New PAC-Bayesian Perspective on Domain Adaptation. CoRR abs/1506.04573 (2015) - 2014
- [c34]Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy:
PAC-Bayesian Theory for Transductive Learning. AISTATS 2014: 105-113 - [c33]Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle:
Agnostic Bayesian Learning of Ensembles. ICML 2014: 611-619 - [c32]Hamidreza Chinaei, Luc Lamontagne, François Laviolette, Richard Khoury:
A Topic Model Scoring Approach for Personalized QA Systems. TSD 2014: 84-92 - [c31]Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette:
Sequential Model-Based Ensemble Optimization. UAI 2014: 440-448 - [i10]Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand:
Sequential Model-Based Ensemble Optimization. CoRR abs/1402.0796 (2014) - [i9]Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil:
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine. CoRR abs/1412.1074 (2014) - [i8]Sébastien Giguère, Amélie Rolland, François Laviolette, Mario Marchand:
On the String Kernel Pre-Image Problem with Applications in Drug Discovery. CoRR abs/1412.1463 (2014) - [i7]Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand:
Domain-Adversarial Neural Networks. CoRR abs/1412.4446 (2014) - 2013
- [j19]Élénie Godzaridis, Sébastien Boisvert, Fangfang Xia, Mikhail Kandel, Steve Behling, Bill Long, Carlos P. Sosa, François Laviolette, Jacques Corbeil:
Human Analysts at Superhuman Scales: What Has Friendly Software To Do? Big Data 1(4): 227-236 (2013) - [j18]Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, Jacques Corbeil:
Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC Bioinform. 14: 82 (2013) - [j17]Josée Desharnais, François Laviolette, Sami Zhioua:
Testing probabilistic equivalence through Reinforcement Learning. Inf. Comput. 227: 21-57 (2013) - [j16]Guy Lever, François Laviolette, John Shawe-Taylor:
Tighter PAC-Bayes bounds through distribution-dependent priors. Theor. Comput. Sci. 473: 4-28 (2013) - [c30]Sébastien Giguère, François Laviolette, Mario Marchand, Khadidja Sylla:
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction. ICML (1) 2013: 107-114 - [c29]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. ICML (3) 2013: 738-746 - [c28]Maxime Latulippe, Alexandre Drouin, Philippe Giguère, François Laviolette:
Accelerated Robust Point Cloud Registration in Natural Environments through Positive and Unlabeled Learning. IJCAI 2013: 2480-2487 - 2012
- [j15]Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer:
PAC-Bayesian Inequalities for Martingales. IEEE Trans. Inf. Theory 58(12): 7086-7093 (2012) - [c27]Pascal Germain, Sébastien Giguère, Jean-Francis Roy, Brice Zirakiza, François Laviolette, Claude-Guy Quimper:
A Pseudo-Boolean Set Covering Machine. CP 2012: 916-924 - [c26]Michael Morin, Anika-Pascale Papillon, Irène Abi-Zeid, François Laviolette, Claude-Guy Quimper:
Constraint Programming for Path Planning with Uncertainty - Solving the Optimal Search Path Problem. CP 2012: 988-1003 - [c25]Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer:
PAC-Bayesian Inequalities for Martingales. UAI 2012: 12 - [c24]Yevgeny Seldin, Nicolò Cesa-Bianchi, Peter Auer, François Laviolette, John Shawe-Taylor:
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits. ICML On-line Trading of Exploration and Exploitation 2012: 98-111 - [c23]Alexandre Lacoste, François Laviolette, Mario Marchand:
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets. AISTATS 2012: 665-675 - [i6]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) - [i5]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayesian Learning and Domain Adaptation. CoRR abs/1212.2340 (2012) - 2011
- [j14]Josée Desharnais, François Laviolette, Amélie Turgeon:
A logical duality for underspecified probabilistic systems. Inf. Comput. 209(5): 850-871 (2011) - [c22]Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian:
A PAC-Bayes Sample-compression Approach to Kernel Methods. ICML 2011: 297-304 - [c21]Jean-Francis Roy, François Laviolette, Mario Marchand:
From PAC-Bayes Bounds to Quadratic Programs for Majority Votes. ICML 2011: 649-656 - [c20]Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, Ronald Ortner:
PAC-Bayesian Analysis of Contextual Bandits. NIPS 2011: 1683-1691 - [i4]Yevgeny Seldin, François Laviolette, John Shawe-Taylor, Jan Peters, Peter Auer:
PAC-Bayesian Analysis of Martingales and Multiarmed Bandits. CoRR abs/1105.2416 (2011) - [i3]Yevgeny Seldin, Nicolò Cesa-Bianchi, François Laviolette, Peter Auer, John Shawe-Taylor, Jan Peters:
PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off. CoRR abs/1105.4585 (2011) - [i2]Yevgeny Seldin, Nicolò Cesa-Bianchi, Peter Auer, François Laviolette, John Shawe-Taylor:
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits. CoRR abs/1110.6755 (2011) - [i1]Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer:
PAC-Bayesian Inequalities for Martingales. CoRR abs/1110.6886 (2011) - 2010
- [j13]Sébastien Boisvert, François Laviolette, Jacques Corbeil:
Ray: Simultaneous Assembly of Reads from a Mix of High-Throughput Sequencing Technologies. J. Comput. Biol. 17(11): 1519-1533 (2010) - [j12]François Laviolette, Mario Marchand, Mohak Shah, Sara Shanian:
Learning the set covering machine by bound minimization and margin-sparsity trade-off. Mach. Learn. 78(1-2): 175-201 (2010) - [c19]Guy Lever, François Laviolette, John Shawe-Taylor:
Distribution-Dependent PAC-Bayes Priors. ALT 2010: 119-133 - [c18]Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin:
Learning with Randomized Majority Votes. ECML/PKDD (2) 2010: 162-177
2000 – 2009
- 2009
- [c17]Josée Desharnais, François Laviolette, Amélie Turgeon:
A Demonic Approach to Information in Probabilistic Systems. CONCUR 2009: 289-304 - [c16]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
PAC-Bayesian learning of linear classifiers. ICML 2009: 353-360 - [c15]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian:
From PAC-Bayes Bounds to KL Regularization. NIPS 2009: 603-610 - [c14]Sami Zhioua, Doina Precup, François Laviolette, Josée Desharnais:
Learning the Difference between Partially Observable Dynamical Systems. ECML/PKDD (2) 2009: 664-677 - 2008
- [c13]François Laviolette, Mario Marchand, Sara Shanian:
Selective Sampling for Classification. Canadian AI 2008: 191-202 - [c12]François Laviolette, Ludovic Tobin:
A Stochastic Point-Based Algorithm for POMDPs. Canadian AI 2008: 332-343 - [c11]Massih-Reza Amini, François Laviolette, Nicolas Usunier:
A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning. NIPS 2008: 65-72 - [c10]Josée Desharnais, François Laviolette, Mathieu Tracol:
Approximate Analysis of Probabilistic Processes: Logic, Simulation and Games. QEST 2008: 264-273 - 2007
- [j11]François Laviolette, Mario Marchand:
PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers. J. Mach. Learn. Res. 8: 1461-1487 (2007) - [j10]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. J. Mach. Learn. Res. 8: 2533-2549 (2007) - 2006
- [j9]Vincent Danos, Josée Desharnais, François Laviolette, Prakash Panangaden:
Bisimulation and cocongruence for probabilistic systems. Inf. Comput. 204(4): 503-523 (2006) - [c9]Josée Desharnais, François Laviolette, Krishna Priya Darsini Moturu, Sami Zhioua:
Trace Equivalence Characterization Through Reinforcement Learning. Canadian AI 2006: 371-382 - [c8]Massih-Reza Amini, Nicolas Usunier, François Laviolette, Alexandre Lacasse, Patrick Gallinari:
A Selective Sampling Strategy for Label Ranking. ECML 2006: 18-29 - [c7]Josée Desharnais, François Laviolette, Sami Zhioua:
Testing Probabilistic Equivalence Through Reinforcement Learning. FSTTCS 2006: 236-247 - [c6]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
A PAC-Bayes Risk Bound for General Loss Functions. NIPS 2006: 449-456 - [c5]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 - 2005
- [j8]François Laviolette:
Decompositions of infinite graphs: I - bond-faithful decompositions. J. Comb. Theory B 94(2): 259-277 (2005) - [j7]François Laviolette:
Decompositions of infinite graphs: Part II circuit decompositions. J. Comb. Theory B 94(2): 278-333 (2005) - [c4]François Laviolette, Mario Marchand, Mohak Shah:
Margin-Sparsity Trade-Off for the Set Covering Machine. ECML 2005: 206-217 - [c3]François Laviolette, Mario Marchand:
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. ICML 2005: 481-488 - [c2]