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

Artémis Llamosi 1, 2 Adel Mezine 2 Florence d'Alché-Buc 2, 3 Véronique Letort 4 Michèle Sebag 5, 3 
3 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : 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.
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https://hal.inria.fr/hal-01109775
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Submitted on : Tuesday, January 27, 2015 - 6:44:54 PM
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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⟩

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