Emilie Morvant

According to our database1, Emilie Morvant authored at least 34 papers between 2011 and 2021.

Collaborative distances:



In proceedings 
PhD thesis 


On csauthors.net:


Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound.
CoRR, 2021

A PAC-Bayes Analysis of Adversarial Robustness.
CoRR, 2021

A General Framework for the Derandomization of PAC-Bayesian Bounds.
CoRR, 2021

Metric Learning from Imbalanced Data with Generalization Guarantees.
Pattern Recognit. Lett., 2020

PAC-Bayes and domain adaptation.
Neurocomputing, 2020

A survey on domain adaptation theory.
CoRR, 2020

Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters.
Neurocomputing, 2019

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting.
CoRR, 2019

Metric Learning from Imbalanced Data.
Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization.
Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Risk upper bounds for general ensemble methods with an application to multiclass classification.
Neurocomputing, 2017

PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

A New PAC-Bayesian Perspective on Domain Adaptation.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Domain adaptation of weighted majority votes via perturbed variation-based self-labeling.
Pattern Recognit. Lett., 2015

On Generalizing the C-Bound to the Multiclass and Multi-label Settings.
CoRR, 2015

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers.
CoRR, 2015

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context.
CoRR, 2015

Learning a priori constrained weighted majority votes.
Mach. Learn., 2014

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

Majority Vote of Diverse Classifiers for Late Fusion.
Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2014

Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

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).
PhD thesis, 2013

Domain Adaptation of Majority Votes via Perturbed Variation-based Label Transfer.
CoRR, 2013

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers.
Proceedings of the 30th International Conference on Machine Learning, 2013

The Multi-Task Learning View of Multimodal Data.
Proceedings of the Asian Conference on Machine Learning, 2013

Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions.
Knowl. Inf. Syst., 2012

PAC-Bayesian Learning and Domain Adaptation
CoRR, 2012

PAC-Bayesian Majority Vote for Late Classifier Fusion
CoRR, 2012

PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification.
Proceedings of the 29th International Conference on Machine Learning, 2012

VideoSense at TRECVID 2011: Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking.
Proceedings of the 2011 TREC Video Retrieval Evaluation, 2011

On the Usefulness of Similarity Based Projection Spaces for Transfer Learning.
Proceedings of the Similarity-Based Pattern Recognition - First International Workshop, 2011

Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions.
Proceedings of the 11th IEEE International Conference on Data Mining, 2011