Emilie Morvant

According to our database, Emilie Morvant authored at least 37 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A general framework for the practical disintegration of PAC-Bayesian bounds.

Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

Mach. Learn., February, 2024

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures.

Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi

CoRR, 2024

2021

A General Framework for the Derandomization of PAC-Bayesian Bounds.

Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

CoRR, 2021

Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound.

Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound.

Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A PAC-Bayes Analysis of Adversarial Robustness.

Paul Viallard, Guillaume Vidot, Amaury Habrard, Emilie Morvant

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Metric Learning from Imbalanced Data with Generalization Guarantees.

Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban

Pattern Recognit. Lett., 2020

PAC-Bayes and domain adaptation.

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

Neurocomputing, 2020

A survey on domain adaptation theory.

Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani

CoRR, 2020

Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting.

Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters.

Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini

Neurocomputing, 2019

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting.

Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

CoRR, 2019

Metric Learning from Imbalanced Data.

Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior.

Gaël Letarte, Emilie Morvant, Pascal Germain

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization.

Anil Goyal, Emilie Morvant, Massih-Reza Amini

Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

2017

Risk upper bounds for general ensemble methods with an application to multiclass classification.

François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy

Neurocomputing, 2017

PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach.

Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

2016

A New PAC-Bayesian Perspective on Domain Adaptation.

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Domain adaptation of weighted majority votes via perturbed variation-based self-labeling.

Emilie Morvant

Pattern Recognit. Lett., 2015

On Generalizing the C-Bound to the Multiclass and Multi-label Settings.

François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy

CoRR, 2015

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

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

CoRR, 2015

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context.

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

CoRR, 2015

2014

Learning a priori constrained weighted majority votes.

Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban

Mach. Learn., 2014

Proceedings of The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM), 2014.

Vladimir Kolmogorov, Christoph H. Lampert, Emilie Morvant, Rustem Takhanov

CoRR, 2014

Majority Vote of Diverse Classifiers for Late Fusion.

Emilie Morvant, Amaury Habrard, Stéphane Ayache

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2014

Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks.

Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Apprentissage de vote de majorité pour la classification supervisée et l'adaptation de domaine : approches PAC-Bayésiennes et combinaison de similarités. (Learning Majority Vote for Supervised Classification and Domain Adaptation: PAC-Bayesian Approaches and Similarity Combination).

Emilie Morvant

PhD thesis, 2013

Domain Adaptation of Majority Votes via Perturbed Variation-based Label Transfer.

Emilie Morvant

CoRR, 2013

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers.

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

Proceedings of the 30th International Conference on Machine Learning, 2013

The Multi-Task Learning View of Multimodal Data.

Hachem Kadri, Stéphane Ayache, Cécile Capponi, Sokol Koço, François-Xavier Dupé, Emilie Morvant

Proceedings of the Asian Conference on Machine Learning, 2013

2012

Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions.

Emilie Morvant, Amaury Habrard, Stéphane Ayache

Knowl. Inf. Syst., 2012

PAC-Bayesian Learning and Domain Adaptation

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

CoRR, 2012

PAC-Bayesian Majority Vote for Late Classifier Fusion

Emilie Morvant, Amaury Habrard, Stéphane Ayache

CoRR, 2012

PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification.

Emilie Morvant, Sokol Koço, Liva Ralaivola

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

VideoSense at TRECVID 2011: Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking.

Emilie Morvant, Stéphane Ayache, Amaury Habrard, Miriam Redi, Claudiu Tanase, Bernard Mérialdo, Bahjat Safadi, Franck Thollard, Nadia Derbas, Georges Quénot

Proceedings of the 2011 TREC Video Retrieval Evaluation, 2011

On the Usefulness of Similarity Based Projection Spaces for Transfer Learning.

Emilie Morvant, Amaury Habrard, Stéphane Ayache

Proceedings of the Similarity-Based Pattern Recognition - First International Workshop, 2011

Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions.

Emilie Morvant, Amaury Habrard, Stéphane Ayache

Proceedings of the 11th IEEE International Conference on Data Mining, 2011