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

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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Communication dans un congrès
Machine Learning and Knowledge Discovery in Databases - Part II, Sep 2014, Nancy, France. Springer Verlag, Lecture Notes in Artificial Intelligence, 8725, pp.306 - 321, 2014, 〈10.1007/978-3-662-44851-9_20〉
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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. Springer Verlag, Lecture Notes in Artificial Intelligence, 8725, pp.306 - 321, 2014, 〈10.1007/978-3-662-44851-9_20〉. 〈hal-01109775〉

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