Fabien Labernia

Fabien Labernia
Paris Dauphine University | UPD · Center for Research in Decision Mathematics

PhD

About

9
Publications
947
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
14
Citations
Introduction
I currently work at WiserSKILLS as a research Engineer. I have a Ph.D. in artificial intelligence at the Center for Research in Decision Mathematics, Paris Dauphine University. I do researches in Data Mining, Artificial Intelligence and Algorithms, and my main project is 'Learning CP-nets'.

Publications

Publications (9)
Article
Faced with both identity theft and the theft of means of authentication, users of digitalservices are starting to look rather suspiciously at online systems. To increase accesssecurity it is necessary to introduce some new factor of implicit authentication such asuser behavior analysis. A behavior is made up of a series of observable actions and ak...
Thesis
Full-text available
La croissance exponentielle des données personnelles, et leur mise à disposition sur la toile, a motivé l’émergence d’algorithmes d’apprentissage de préférences à des fins de recommandation, ou d’aide à la décision. Les réseaux de préférences conditionnelles (CP-nets) fournissent une structure compacte et intuitive pour la représentation de telles...
Conference Paper
Full-text available
We deal with online learning of acyclic Conditional Preference networks (CP-nets) from data streams, possibly corrupted with noise. We introduce a new, efficient algorithm relying on (i) information-theoretic measures defined over the induced preference rules, which allow us to deal with corrupted data in a principled way, and on (ii) the Hoeffding...
Article
Full-text available
Conditional preference networks (CP-nets) provide a compact and intuitive graphical tool to represent the preferences of a user. However, learning such a structure is known to be a difficult problem due to its combinatorial nature. We propose, in this paper, a new, efficient, and robust query-based learning algorithm for acyclic CP-nets. In particu...
Conference Paper
Full-text available
Conditional preference networks (CP-nets) provide a powerful, compact, and intuitive graphical tool to represent the preferences of a user. However learning such a structure is known to be a difficult problem due to its combinatorial nature. We propose in this paper a new, efficient, and robust query-based learning algorithm for acyclic CP-nets. In...
Conference Paper
Full-text available
Nous présentons une méthode de réduction de dimensionnalité pour des données de préférences multicritères lorsque l'espace des évaluations est un treillis distributif borné. Cette méthode vise à réduire la complexité des procédures d'apprentissage d'un modèle d'agrégation sur des données qualita-tives. Ainsi nous considérons comme modèle d'agrégati...
Conference Paper
Full-text available
Faced with both identity theft and the theft of means of authentication, users of digital services are starting to look rather suspiciously at online systems. The behavior is made up of a series of observable actions of an Internet user and, taken as a whole, the most frequent of these actions amount to habit. Habit and reputation oer ways of recog...
Technical Report
Full-text available
A collection of sets on a ground set S n (S n = {1, 2, ..., n}) closed under intersection and containing S n is known as a Moore family. The set of Moore families for a fixed n is a lattice denoted M n. In this paper we provide a recursive definition of M n. This alternative definition puts highlight some new structural properties of the lattice of...

Network

Cited By