Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (ICDM, 2011)

On the Usefulness of Similarity Based Projection Spaces for Transfer Learning. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (SIMBAD, 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 (TRECVID, 2011)

PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification. - Emilie Morvant; Sokol Koço; Liva Ralaivola (ICML, 2012)

PAC-Bayesian Majority Vote for Late Classifier Fusion - Emilie Morvant; Amaury Habrard; Stéphane Ayache (CoRR, 2012)

PAC-Bayesian Learning and Domain Adaptation - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2012)

Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (Knowl. Inf. Syst., 2012)

The Multi-Task Learning View of Multimodal Data. - Hachem Kadri; Stéphane Ayache; Cécile Capponi; Sokol Koço; François-Xavier Dupé; Emilie Morvant (ACML, 2013)

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (ICML (3), 2013)

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

Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. - Mario Marchand; Hongyu Su; Emilie Morvant; Juho Rousu; John Shawe-Taylor (NIPS, 2014)

Majority Vote of Diverse Classifiers for Late Fusion. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (S+SSPR, 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)

Learning a priori constrained weighted majority votes. - Aurélien Bellet; Amaury Habrard; Emilie Morvant; Marc Sebban (Mach. Learn., 2014)

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2015)

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2015)

On Generalizing the C-Bound to the Multiclass and Multi-label Settings. - François Laviolette; Emilie Morvant; Liva Ralaivola; Jean-Francis Roy (CoRR, 2015)

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

A New PAC-Bayesian Perspective on Domain Adaptation. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (ICML, 2016)

PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach. - Anil Goyal; Emilie Morvant; Pascal Germain; Massih-Reza Amini (ECML/PKDD (2), 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)

Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization. - Anil Goyal; Emilie Morvant; Massih-Reza Amini (IDA, 2018)

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior. - Gaël Letarte; Emilie Morvant; Pascal Germain (AISTATS, 2019)

Metric Learning from Imbalanced Data. - Léo Gautheron; Amaury Habrard; Emilie Morvant; Marc Sebban (ICTAI, 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)

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. - Anil Goyal; Emilie Morvant; Pascal Germain; Massih-Reza Amini (Neurocomputing, 2019)

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 (ECML/PKDD (3), 2020)

A survey on domain adaptation theory. - Ievgen Redko; Emilie Morvant; Amaury Habrard; Marc Sebban; Younès Bennani (CoRR, 2020)

PAC-Bayes and domain adaptation. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (Neurocomputing, 2020)

Metric Learning from Imbalanced Data with Generalization Guarantees. - Léo Gautheron; Amaury Habrard; Emilie Morvant; Marc Sebban (Pattern Recognit. Lett., 2020)

A PAC-Bayes Analysis of Adversarial Robustness. - Paul Viallard; Guillaume Vidot; Amaury Habrard; Emilie Morvant (NeurIPS, 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 (NeurIPS, 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 (ECML/PKDD (2), 2021)

A General Framework for the Derandomization of PAC-Bayesian Bounds. - Paul Viallard; Pascal Germain; Amaury Habrard; Emilie Morvant (CoRR, 2021)

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. - Paul Viallard; Rémi Emonet; Amaury Habrard; Emilie Morvant; Valentina Zantedeschi (AISTATS, 2024)

A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint. - Jordan Patracone; Paul Viallard; Emilie Morvant; Gilles Gasso; Amaury Habrard; Stéphane Canu (ECML/PKDD (4), 2024)

A general framework for the practical disintegration of PAC-Bayesian bounds. - Paul Viallard; Pascal Germain; Amaury Habrard; Emilie Morvant (Mach. Learn., 2024)

Avancées en théorie PAC-Bayésienne : de bornes en généralisation à des algorithmes d'apprentissage supervisé et de transfert. (Advances in PAC-Bayesian theory from generalization bounds to supervised and transfer learning algorithms). - Emilie Morvant (2025)

PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures. - Hind Atbir; Farah Cherfaoui; Guillaume Metzler; Emilie Morvant; Paul Viallard (CoRR, 2025)