Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach

Résumé

This study focuses on dynamical system identification, with the reverse modeling of a gene regulatory network as motivating appli-cation. An active learning approach is used to iteratively select the most informative experiments needed to improve the parameters and hidden variables estimates in a dynamical model given a budget for experiments. The design of experiments under these budgeted resources is formalized in terms of sequential optimization. A local optimization criterion (re-ward) is designed to assess each experiment in the sequence, and the global optimization of the sequence is tackled in a game-inspired setting, within the Upper Confidence Tree framework combining Monte-Carlo tree-search and multi-armed bandits. The approach, called EDEN for Experimental Design for parameter Estimation in a Network, shows very good performances on several re-alistic simulated problems of gene regulatory network reverse-modeling, inspired from the international challenge DREAM7.
Fichier principal
Vignette du fichier
dalcheSebagECML2014-author.pdf (572.89 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01109775 , version 1 (27-01-2015)

Identifiants

Citer

Artémis Llamosi, Adel Mezine, Florence d'Alché-Buc, Véronique Letort, Michèle Sebag. Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach. Machine Learning and Knowledge Discovery in Databases - Part II, Sep 2014, Nancy, France. pp.306 - 321, ⟨10.1007/978-3-662-44851-9_20⟩. ⟨hal-01109775⟩
808 Consultations
503 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More