Nadjib Lazaar
University of Montpellier
51 Papers
125 Citations
Nadjib Lazaar is an academic researcher from University of Montpellier. The author has contributed to research in topics: Constraint (information theory) & Constraint programming. The author has an hindex of 9, co-authored 40 publications. Previous affiliations of Nadjib Lazaar include Microsoft & Centre national de la recherche scientifique.
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Papers
•Proceedings Article
Constraint acquisition via partial queries
Christian Bessiere,Remi Coletta,Emmanuel Hebrard,George Katsirelos,Nadjib Lazaar,Nina Narodytska,Claude-Guy Quimper,Toby Walsh +7 more
- 03 Aug 2013
TL;DR: This work provides an algorithm that, given a negative example, focuses onto a constraint of the target network in a number of queries logarithmic in the size of the example.
A Global Constraint for Closed Frequent Pattern Mining
Nadjib Lazaar,Yahia Lebbah,Samir Loudni,Mehdi Maamar,Mehdi Maamar,Valentin Lemière,Christian Bessiere,Patrice Boizumault +7 more
- 05 Sep 2016
TL;DR: This paper proposes the ClosedPattern global constraint to capture the closed frequent pattern mining problem without requiring reified constraints or extra variables, and presents an algorithm to enforce domain consistency on ClosedPattern in polynomial time.
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New Approaches to Constraint Acquisition
Christian Bessiere,Abderrazak Daoudi,Emmanuel Hebrard,George Katsirelos,Nadjib Lazaar,Younes Mechqrane,Nina Narodytska,Claude-Guy Quimper,Toby Walsh +8 more
- 01 Jan 2016
TL;DR: This chapter presents the recent results on constraint acquisition obtained by the Coconut team and their collaborators, and shows how to learn constraint networks by asking the user partial queries, and introduces the concept of generalization query based on an aggregation of variables into types.
•Proceedings Article
Multiple constraint acquisition
Robin Arcangioli,Christian Bessiere,Nadjib Lazaar +2 more
- 27 Jul 2015
TL;DR: A new approach is provided that is able to learn a maximum number of constraints violated by a given negative example and an experimental evaluation shows that this approach improves the state of the art.
Discovering Program Topoi via Hierarchical Agglomerative Clustering
TL;DR: This paper implemented FEAT on top of a general-purpose test management and optimization platform and performed an experimental study over 15 open-source software projects proving that automatically discovering topoi is feasible and meaningful on realistic projects.