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HDR (in french, Habilitation à Diriger des Recherches)

• 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
  HDR committee: Marianne Clausel (reviewer), Stéphane Chrétien (chair, examiner), Colin De La Higuera (reviewer), Rémi Emonet (examiner), François Jacquenet (tutor), Liva Ralaivola (reviewer)
  Defense date: April 7, 2025
  pdf bibtex slides

Ph.D. Thesis (in french)

• 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 committee: Stéphane Ayache (co-advisor), Antoine Cornuéjols (chair, examiner), Rémi Gilleron (examiner), Amaury Habrard (advisor),
  Mario Marchand (reviewer), Liva Ralaivola (examiner), Michèle Sebag (reviewer).
  Defense date: September 18, 2013
  Ph.D. award 2013 from Aix-Marseille University
  A runner-up award of the “Prix de thèse AFIA 2014
  pdf bibtex slides

Book

• Domain Adaptation Theory: Available Theoretical Results - Ievgen Redko ; Emilie Morvant ; Amaury Habrard ; Marc Sebban ; Younès Bennani
  ISTE Press-Elsevier, 2019, ISBN : 9781785482366
  publisher’s website bibtex short version- ArXiV

International Journals

• A General Framework for the Practical Disintegration of PAC-Bayesian Bounds - Paul Viallard ; Pascal Germain ; Amaury Habrard ; Emilie Morvant
  Machine Learning Journal (MLJ), 113:519–604, 2024, DOI : 10.1007/s10994-023-06391-0 (presented also at ECML-PKDD 2023)
  published version research report bibtex

• Metric Learning from Imbalanced Data with Generalization Guarantees - Léo Gautheron ; Amaury Habrard ; Emilie Morvant ; Marc Sebban
  Pattern Recognition Letters (PRL), 133:298-304, 2020, DOI : 10.1016/j.patrec.2020.03.008
  published version bibtex

• PAC-Bayes and Domain Adaptation - Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  Neurocomputing, 379:379-397, 2020, DOI : 10.1016/j.neucom.2019.10.105
  pdf published version bibtex

• Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters - Anil Goyal ; Emilie Morvant ; Pascal Germain ; Massih-Reza Amini
  Neurocomputing, 358:81-92, 2019, DOI : 10.1016/j.neucom.2019.04.072
  pdf published version bibtex

• Risk Upper Bounds for General Ensemble Methods with an application to Multiclass Classification - François Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy
  Neurocomputing, 219:15-25, 2017, DOI: 10.1016/j.neucom.2016.09.016
  pdf published version bibtex

• Domain Adaptation of Weighted Majority Votes via Perturbed Variation-Based Self-Labeling - Emilie Morvant
  Pattern Recognition Letters (PRL), 51(0):37–43, 2015, DOI: 10.1016/j.patrec.2014.08.013
  pdf published version bibtex

• Learning A Priori Constrained Weighted Majority Votes - Aurélien Bellet ; Amaury Habrard ; Emilie Morvant ; Marc Sebban
  Machine Learning Journal (MLJ), 97(1-2):129-154, 2014, DOI: 10.1007/s10994-014-5462-z
  pdf published version bibtex

• Parsimonious Unsupervised and Semi-Supervised Domain Adaptation with Good Similarity Functions - Emilie Morvant ; Amaury Habrard ; Stéphane Ayache
  Knowledge and Information Systems (KAIS), 33(2):309-349, 2012, DOI: 10.1007/s10115-012-0516-7
  pdf published version bibtex

International Conferences

• A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint - Jordan Patracone ; Paul Viallard ; Emilie Morvant ; Gilles Gasso ; Amaury Habrard ; Stéphane Canu
  European Conference on Machine Learning and Principles and Pratice of Knowledge Discovery in Databases (ECML-PKDD), 2024
  pdf bibtex

• Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures - Paul Viallard ; Rémi Emonet ; Amaury Habrard ; Emilie Morvant ; Valentina Zantedeschi
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
  pdf bibtex code

• 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
  Conference on Neural Information Processing Systems (NeurIPS), 2021
  pdf bibtex code

• A PAC-Bayes Analysis of Adversarial Robustness - Guillaume Vidot ; Paul Viallard ; Amaury Habrard ; Emilie Morvant
  Conference on Neural Information Processing Systems (NeurIPS), 2021
  pdf bibtex code

• 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
  European Conference on Machine Learning and Principles and Pratice of Knowledge Discovery in Databases (ECML-PKDD), 2021
  pdf bibtex

• 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
  European Conference on Machine Learning and Principles and Pratice of Knowledge Discovery in Databases (ECML-PKDD), 2020
  pdf bibtex

• Metric Learning from Imbalanced Data - Léo Gautheron ; Amaury Habrard ; Emilie Morvant ; Marc Sebban
  The IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2019, Portland, Oregon, USA
  pdf bibtex

• Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior - Gaël Letarte ; Emilie Morvant ; Pascal Germain
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2019, Naha, Okinawa, Japan
  pdf bibtex research report arXiv:1810.12683

• Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization - Anil Goyal ; Emilie Morvant ; Massih-Reza Amini
  International Symposium on Intelligent Data Analysis (IDA), 2018, ‘s-Hertogenbosch, the Netherlands
  pdf bibtex

• PAC-Bayesian Analysis for a two-step Hierarchical Mutliview Learning Approach - Anil Goyal ; Emilie Morvant ; Pascal Germain ; Massih-Reza Amini
  European Conference on Machine Learning and Principles and Pratice of Knowledge Discovery in Databases (ECML-PKDD), 2017, Skopje, Macedonia
  pdf bibtex research report arXiv:1606.07240

• A New PAC-Bayesian Perspective on Domain Adaptation - Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  International Conference on Machine Learning (ICML), 2016, New York, USA
  pdf bibtex research report arXiv:1506.04573 code

• Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks - Mario Marchand ; Su Hongyu ; Emilie Morvant ; Juho Rousu ; John Shawe-Taylor
  Neural Information Processing Systems (NIPS), 2014, Montréal, Canada
  pdf bibtex

• A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers - Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  International Conference on Machine Learning (ICML), 2013, Atlanta, USA
  pdf bibtex code

• The Multi-Task Learning View of Multimodal Data - Hachem Kadri ; Stéphane Ayache ; Cécile Capponi ; Sokol Koço ; François-Xavier Dupé ; Emilie Morvant
  Asian Conference on Machine Learning (ACML), 2013, Canberra, Australia
  pdf bibtex

• PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification - Emilie Morvant ; Sokol Koço ; Liva Ralaivola
  International Conference on Machine Learning (ICML), 2012, Edinburgh, United Kingdom. pp. 815-822
  pdf bibtex research report arXiv:1202.6228

• Sparse Domain Adaptation in Projection Spaces based on Good Similarity Functions - Emilie Morvant ; Amaury Habrard; Stéphane Ayache<
  IEEE International Conference on Data Mining series (ICDM), 2011, Vancouver, Canada. IEEE Computer Society, pp. 457-466
  Selected as one of the best papers for possible publication in Knowledge and Information Systems (KAIS).
  pdf bibtex

International Workshops

Published

• Majority Vote of Diverse Classifiers for Late Fusion - Emilie Morvant ; Amaury Habrard ; Stéphane Ayache
  IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), 2014, Joensuu, Finland.
  pdf bibtex research report arXiv:1207.1019

• On the Usefulness of Similarity Based Projection Spaces for Transfer Learning - Emilie Morvant ; Amaury Habrard ; Stéphane Ayache
  First International Workshop on Similarity-Based Pattern Recognition, 2011, Venise, Italy.
  Organized by the EU FP7 Project SIMBAD
  pdf bibtex video

Challenge

• VideoSense at TRECVID 2011: Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking - Emilie Morvant; Stéphane Ayache; Amaury Habrard; Miriam Redi; Tanase Claudiu; Bernard Merialdo; Bahjat Safadi; Franck Thollard; Nadia Derbas; Georges Quenot
  TRECVID Workshop participants notebook papers, NIST, 2012
  pdf bibtex

Not Published

• Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory - Paul Viallard ; Rémi Emonet ; Pascal Germain ; Amaury Habrard ; Emilie Morvant
  NeurIPS 2019 Workshop on Machine Learning with guarantees, Vancouver, Canada.
  pdf

• A New PAC-Bayesian View of Domain Adaptation - Pascal Germain ; François Laviolette ; Amaury Habrard ; Emilie Morvant
  NIPS 2015 Workshop on (https://sites.google.com/site/tlworkshop2015/)Transfer and Multi-Task Learning: Trends and New Perspectives, Montréal, Canada.
  research report code
  Finalized version published at ICML’16

• On Generalizing the C-Bound to the Multiclass and Multi-label Settings - François Laviolette ; Emilie Morvant ; Liva Ralaivola ; Jean-Francis Roy
  NIPS 2014 Workshop on Representation and Learning Methods for Complex Outputs, Montréal, Canada.
  pdf research report

• An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context - Pascal Germain ; François Laviolette ; Amaury Habrard ; Emilie Morvant
  NIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Montréal, Canada.
  pdf research report

• Domain Adaptation of Majority Votes via Perturbed Variation-based Label transfer - Emilie Morvant
  NIPS 2013 Workshop (https://sites.google.com/site/learningacross/hom)New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks, Lake Tahoe, Nevada, United States.
  [(https://hal.archives-ouvertes.fr/hal-00906188/document)pdf
  Finalized version published in PRL’15

• PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers
  Emilie Morvant (Pascal Germain ; Amaury Habrard ; François Laviolette)
  (https://wimlworkshop.org/)Women in Machine Learning workshop (WiML 2013), Lake Tahoe, Nevada, United States.
  Presentation of our ICML’13

• PAC-Bayesian Learning and Domain Adaptation - Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  NIPS 2012 Workshop (https://sites.google.com/site/multitradeoffs2012/)Multi-trade-off in Machine Learning, Lake Tahoe, Nevada, United States.
  pdf poster
  Finalized version published at ICML’13

• Generalization of the C-bound to Multiclass Setting
  Emilie Morvant (François Laviolette ; Liva Ralaivola ; Jean-Francis Roy)
  Women in Machine Learning workshop (WiML 2012), Lake Tahoe, Nevada, United States.

• Sparse Domain Adaptation in a Good Similarity-Based Projection Space - Emilie Morvant ; Amaury Habrard ; Stéphane Ayache
  NIPS 2011 Domain Adaptation Workshop, Sierra Nevada, Spain.
  pdf poster
  Finalized version published at ICDM’11

French Conferences

• Une borne PAC-Bayésienne sur une mesure de risque pour l'apprentissage équitable - Hind Atbir ; Farah Cherfaoui ; Guillaume Metzler ; Emilie Morvant ; Paul Viallard
  French Conference on Machine Learning (CAp 2022), 2022

• Intérêt des bornes désintégrées pour la généralisation avec des mesures de complexité - Paul Viallard ; Rémi Emonet ; Pascal Germain ; Amaury Habrard ; Emilie Morvant ; Valentina Zantedeschi
  French Conference on Machine Learning (CAp 2022), 2022

• 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
  French Conference on Machine Learning (CAp 2022), 2022

• Apprentissage de Vote de Majorité par Minimisation d’une C-Borne - Paul Viallard ; Emilie Morvant ; Pascal Germain
  French Conference on Machine Learning (CAp 2021), 2021

• Dérandomisation des Bornes PAC-Bayésiennes - Paul Viallard ; Emilie Morvant ; Pascal Germain
  French Conference on Machine Learning (CAp 2021), 2021

• Une Analyse PAC-Bayésienne de la Robustesse Adversariale - Guillaume Vidot ; Paul Viallard ; Emilie Morvant
  French Conference on Machine Learning (CAp 2021), 2021

• Théorie PAC-Bayésienne pour l'apprentissage en deux étapes de réseaux de neurones - Paul Viallard ; Rémi Emonet ; Amaury Habrard ; Emilie Morvant; Pascal Germain
  French Conference on Machine Learning (CAp 2020), 2020

• Apprentissage d'ensemble basé sur des points de repère avec des caractéristiques de Fourier aléatoires et un renforcement du gradient - Léo Gautheron ; Pascal Germain ; Amaury Habrard ; Guillaume Metzler ; Emilie Morvant ; Marc Sebban ; Valentina Zantedeschi</titre>__ - French Conference on Machine Learning (CAp 2020), 2020

• Revisite des "random Fourier features" basée sur l'apprentissage PAC-Bayésien via des points d'intérêts - Léo Gautheron, Pascal Germain, Amaury Habrard, Gaël Letarte, Emilie Morvant, Marc Sebban, Valentina Zantedeschi
  French Conference on Machine Learning (CAp 2019), 2019, Toulouse, France.

• Apprentissage d’un vote de majorité hiérarchique pour l’apprentissage multivue - Anil Goyal, Emilie Morvant, Massih-Reza Amini
  French Conference on Machine Learning (CAp 2018), 2018, Rouen, France.

• Apprentissage de métrique pour la classification supervisée de données déséquilibrées - Léo Gautheron ; Amaury Habrard ; Emilie Morvant ; Marc Sebban
  French Conference on Machine Learning (CAp 2018), 2018, Rouen, France.

• Une borne PAC-Bayésienne en espérance et son extension à l'apprentissage multivues - Anil Goyal, Emilie Morvant, Pascal Germain
  French Conference on Machine Learning (CAp 2017), 2017, Grenoble, France.

• Théorèmes PAC-Bayésiens pour l'apprentissage multi-vues - Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini
  French Conference on Machine Learning (CAp 2016), 2016, Marseille, France.

• Adaptation de domaine de vote de majorité par auto-étiquetage non itératif - Emilie Morvant
  French Conference on Machine Learning (CAp 2014), 2014, Saint-Etienne, France.

• Une analyse PAC-Bayésienne de l’adaptation de domaine et sa spécialisation aux classifieurs linéaires - Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  French Conference on Machine Learning (CAp 2013), 2013, Lille, France.

• Vote de majorité a priori contraint pour la classification binaire : spécification au cas des plus proches voisins. - Aurélien Bellet ; Amaury Habrard ; Emilie Morvant ; Marc Sebban
  French Conference on Machine Learning (CAp 2013), 2013, Lille, France.

• Étude de la généralisation de DASF à l'adaptation de domaine semi-supervisée - Emilie Morvant ; Amaury Habrard ; Stéphane Ayache
  French Conference on Machine Learning (CAp 2012), 2012, Nancy, France.

• Adaptation de domaine parcimonieuse par pondération de bonnes fonctions de similarité - Emilie Morvant ; Stéphane Ayache ; Amaury Habrard
  French Conference on Machine Learning (CAp 2011), 2011, Chambéry, France.

Research Reports— not (yet) published

• PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers - Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  research report (2015) Extended version of our ICML’13

Miscellaneous

• Proceedings of The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM), 2014 - Vladimir Kolmogorov ; Christoph Lampert ; Emilie Morvant ; Rustem Takhanov
  proceedings

• Flexible Domain Adaptation for Multimedia Indexing (Best Poster Award, presentation of a part of our ICDM’11 paper)
  Emilie Morvant ; Amaury Habrard ; Stéphane Ayache
  Poster presentation at ENS/INRIA Computer Vision and Machine Learning Summer School, 2011.
  poster\