Emilie Morvant
               Assistant Professor (Maître de Conférences) in Machine Learning

Machine Learning Transfer Learning, Domain Adaptation PAC-Bayes Bounds, Methods (Semi-)Supervised Learning Robustness, Adversarial learning Representation Learning Similarity Functions/Metric Learning Deep Learning Multiview/Multimodality Learning Fairness and biais in ML Highly unbalanced data (anomaly/fraud detection)

Research Projects ANR project Famous - local coordinator Fair multimodal Learning Projet inter-laboratoire FIL (Fédération Informatique de Lyon) (with G. Metzler) PAC-Bayesian Fair Learning Projet inter-laboratoire FIL (Fédération Informatique de Lyon) (with G. Metzler) Random Fourrier Features for PAC-Bayesian Domain Adaptation ANR project TAUDoS - member Theory and Algorithms for the Understanding of Deep learning On Sequential data ANR project APRIORI - coordinator A Pac-bayesian RepresentatIOn leaRnIng perspective INS2I-CNRS project JCJC 2018 "PaRaFF" (with P. Germain) PAC-Bayesian Random Fourier Features ANR project LIVES - member Learning with Interacting ViEwS My postdoc was funded by the ERC grant no308036: Life-Long Learning for Visual Scene Understanding I was a member of the PASCAL2 Network of excellence My Ph.D. was funded by VideoSense ANR project: Rich concepts recognition in multilingual videos, throw pivot languages I regularly participated in Lampada ANR Project: Learning Algorithms, Models and sPArse representations for structured DAta

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Supervision

PostDoc

Marie-Ange Lebre --- Deep Learning for detection and classification of microorganism (since September 2022, co-supervised with A. Habrard and R. Emonet)

Ph.D. Student

Hind Atbir --- Learning fair and robust kernel-based models with generalization guarantees (from October 2024 to ... , co-supervised with R. Eyraud and F. Cherfaoui)

Julien Bastian --- Multiview Fair Learning - From Theory to Algorithms (from October 2024 to ... , co-supervised with C. Largeron and G. Metzler)

Former Ph.D. Students

Paul Viallard --- Beyond PAC-Bayesian Bounds: From Disintegration to Novel Bounds (from September 2019 to December 2022, co-supervised with A. Habrard and P. Germain) --- Now IFSP Inria Rennes

Léo Gautheron --- Learning Tailored Data Representations from Few Labeled Examples (from October 2017 to December 2020, co-supervised with Pr. A. Habrard and Pr. M. Sebban) --- Now Data Science Engineer at Synapse Défense

Anil Goyal --- Learning a Multiview Weighted Majority Vote Classifier: Using PAC-Bayesian Theory and Boosting (from November 2015 to October 2018, co-supervised with Pr. Massih-Reza Amini) --- Now Lead Data Scientist at Housing.com (India)

Interns

Julien Bastian --- Fairness and domain generalization (Mar-Aug 2024, co-supervised with G. Metzler)
Hind Atbir --- PAC-Bayesian Fair Learning (Feb-July 2024, co-supervised with F. Cherfaoui, G. Metzler and P. Viallard)
Mickaël Gault --- Learning fair kernel classifier under constraints (Mar-July 2024, co-supervised with G. Metzler)
Julien Bastian --- Random Fourier Features, PAC-Bayes and Domain Adaptation (Mar-July 2023, co-supervised with G. Metzler)
Alexiane Fraisse --- Random Fourier Features and Domain Adaptation (Apr-July 2022, co-supervised with G. Metzler and P. Viallard)
Luiza Dzhidzhavadze --- A Multiclass C-Bound-Based Algorithm (Apr-June 2021, co-supervised with P. Viallard)
Himanshu Pandey --- A Multiclass C-Bound-Based Algorithm (Apr-June 2021, co-supervised with P. Viallard)
Paul Viallard --- Deep Learning and PAC-Bayes (Feb-June 2019, co-supervised with A. Habrard and R. Emonet)
Omar El-Sabrout --- Active Learning for PAC-Bayesian Domain Adaptation (Mid-April Mid-July 2018)
Loujain Liekah --- Experts Combination (April-June 2018, co-supervised with Dr. M. Soare)
Luc Giffon --- Efficient anomaly detection in data stream (Feb-June 2017, co-supervised with Dr. A. Bonnefoy and Dr. T. Peel)
Arunava Maulik --- (April-June 2017, co-supervised with Pr. A. Habrard and Dr. M. Soare)
Prem Prakash --- (April-June 2017, co-supervised with Pr. A. Habrard)
Léo Gautheron --- Improving the bibliometry platform Labmetry (April-June 2016, co-supervised with Pr. M. Sebban)
Benjamin Sabot --- Empirical study of the C-bound as stopping criterion for neural networks (April-June 2016, co-supervised with Pr. A. Habrard, Dr. P. Germain and D. Fourure)
Soroush Seifi --- A PAC-Bayesian Multiview Study (April-June 2016, co-supervised with Pr. A. Habrard and A. Goyal)

PhD Jurys

2022 Luxin Zhang, Lille (external examinator)
2020 Léo Gautheron, St-Etienne (co-supervisor)
2018 Anil Goyal, St-Etienne/Grenoble (co-supervisor)

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Teaching

Depuis Juillet 2015, je suis la responsable de la 2ème année de la licence d'informatique. Depuis Juillet 2024, je suis également responsable de la 1ère année de la licence d'informatique
Since July 2015, I am in charge of the 2nd year of BSc in Computer Science. Since July 2024, I am also in charge of the 1st year of BSc in Computer Science.

S1

L1 - Programmation fonctionnelle - Ocaml
L2 - Systèmes d'exploitation
L2 - Programmation impérative - C (classique et spécifique alternants)

S2

L2 - Programmation impérative - C
L1 - Programmation impérative - python

Old teaching here - Anciens enseignements ici (not up to date)

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Bibliography    (My DBLP webpage - My Google Scholar webpage)

 •  Ph.D. Thesis  •  Book  •  International Journals  •  International Conferences  •  International Workshops  •  French Conferences  •  Reports  •  Misc.  • 

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 de l'AFIA 2014"
  [pdf] [bibtex] [slides] [abstract/related works]

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 arXiv:2102.08649] [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 & Principles and Pratice of Knowledge Discovery in Databases (ECML-PKDD), 2024
  [research report]
  
  
• 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] [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] [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] [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 & 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 & 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 & 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] [video] [DALC code] [extended journal version]
  
  
• 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] [video] [PBDA code] [extended version]
  
  
• 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] [video] [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. pp. 153-162
  [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. pp. 1-16
  Organized by the EU FP7 Project SIMBAD
  [pdf] [bibtex] [video]

Benchmarks

• 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 Transfer and Multi-Task Learning: Trends and New Perspectives, Montréal, Canada.
  [research report arXiv:1506.04573] [DALC code]
  Finalized version published at ICML'16 [bibtex]
  
  
• 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 arXiv:1408.1336]
  
  
• 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 arXiv:1503.06944]
  
  
• Domain Adaptation of Majority Votes via Perturbed Variation-based Label transfer
  Emilie Morvant
  NIPS 2013 Workshop New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks, Lake Tahoe, Nevada, United States.
  [pdf]
  Finalized version published in PRL [bibtex]
  
  
• PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers
  Emilie Morvant (Pascal Germain ; Amaury Habrard ; François Laviolette)
  Women in Machine Learning workshop (WiML 2013), Lake Tahoe, Nevada, United States.
  Presentation of our ICML'13 paper
  
  
• PAC-Bayesian Learning and Domain Adaptation
  Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  NIPS 2012 Workshop Multi-trade-off in Machine Learning, Lake Tahoe, Nevada, United States.
  [pdf] [poster]
  Finalized version published at ICML'13 [bibtex] [video]
  
  
• 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 [bibtex]

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
  [pdf, published at NeurIPS 21]
  
  
• 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 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. pp. 49-58
  [pdf]
  English version published in PRL [bibtex]
  
  
• 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.
  [pdf] [PBDA code]
  English version published at ICML'13 [bibtex]
  
  
• 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.
  [pdf]
  English version published in MLJ [bibtex]
  
  
• 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. pp. 111-126
  [pdf]
  English version published in KAIS [bibtex]
  
  
• 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. pp. 295-310
  [pdf]
  English version published at ICDM'11 [bibtex]

Research Reports --- not (yet) published

• Generalization Bounds with Arbitrary Complexity Measures
  Paul Viallard ; Rémi Emonet ; Amaury Habrard ; Emilie Morvant ; Valentina Zantedeschi
  [research report]
  
  
• PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers (2015)
  Pascal Germain ; Amaury Habrard ; François Laviolette ; Emilie Morvant
  [research report arXiv:1503.06944]
  Extended version of our ICML'13 paper

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