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papers

Publications (21)

stat.ML2015

On the Generalization of the C-Bound to Structured Output Ensemble Methods

FranÃ§ois Laviolette, Emilie Morvant, Liva Ralaivola +1

stat.ML2012

PAC-Bayesian Learning and Domain Adaptation

Pascal Germain, Amaury Habrard, FranÃ§ois Laviolette +1

q-bio.GN2015

Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance

Alexandre Drouin, SÃ©bastien GiguÃ¨re, Maxime DÃ©raspe +3

cs.LG2014

Sequential Model-Based Ensemble Optimization

Alexandre Lacoste, Hugo Larochelle, FranÃ§ois Laviolette +1

stat.ML2015

Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

Pascal Germain, Alexandre Lacasse, FranÃ§ois Laviolette +2

cs.LG2012

PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits

Yevgeny Seldin, NicolÃ² Cesa-Bianchi, Peter Auer +2

q-bio.GN2014

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

Alexandre Drouin, SÃ©bastien GiguÃ¨re, Vladana Sagatovich +4

cs.LG2012

PAC-Bayesian Inequalities for Martingales

Yevgeny Seldin, FranÃ§ois Laviolette, NicolÃ² Cesa-Bianchi +2

stat.ML2017

Maximum Margin Interval Trees

Alexandre Drouin, Toby Dylan Hocking, FranÃ§ois Laviolette

cs.LG2011

PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off

Yevgeny Seldin, NicolÃ² Cesa-Bianchi, FranÃ§ois Laviolette +3

q-bio.QM2014

Improved design and screening of high bioactivity peptides for drug discovery

SÃ©bastien GiguÃ¨re, FranÃ§ois Laviolette, Mario Marchand +4

cs.LG2011

PAC-Bayesian Analysis of Martingales and Multiarmed Bandits

Yevgeny Seldin, FranÃ§ois Laviolette, John Shawe-Taylor +2

q-bio.GN2016

Large scale modeling of antimicrobial resistance with interpretable classifiers

Alexandre Drouin, FrÃ©dÃ©ric Raymond, GaÃ«l Letarte St-Pierre +3

cs.LG2015

Efficient Learning of Ensembles with QuadBoost

Louis Fortier-Dubois, FranÃ§ois Laviolette, Mario Marchand +2

stat.ML2016

A New PAC-Bayesian Perspective on Domain Adaptation

Pascal Germain, Amaury Habrard, FranÃ§ois Laviolette +1

stat.ML2015

Domain-Adversarial Neural Networks

Hana Ajakan, Pascal Germain, Hugo Larochelle +2

cs.LG2014

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

stat.ML2016

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers

Pascal Germain, Amaury Habrard, FranÃ§ois Laviolette +1

stat.ML2016

Domain-Adversarial Training of Neural Networks

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan +5

q-bio.GN2013

Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Keith R. Bradnam, Joseph N. Fass, Anton Alexandrov +88

q-bio.QM2012

Publications (21)

On the Generalization of the C-Bound to Structured Output Ensemble Methods

PAC-Bayesian Learning and Domain Adaptation

Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance

Sequential Model-Based Ensemble Optimization

Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits

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

PAC-Bayesian Inequalities for Martingales

Maximum Margin Interval Trees

PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off

Improved design and screening of high bioactivity peptides for drug discovery

PAC-Bayesian Analysis of Martingales and Multiarmed Bandits

Large scale modeling of antimicrobial resistance with interpretable classifiers

Efficient Learning of Ensembles with QuadBoost

A New PAC-Bayesian Perspective on Domain Adaptation

Domain-Adversarial Neural Networks

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

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers

Domain-Adversarial Training of Neural Networks

Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

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