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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.
Cited literature [18 references]
https://hal.inria.fr/hal-01109775
Contributor : Marc Schoenauer Connect in order to contact the contributor
Submitted on : Tuesday, January 27, 2015 - 6:44:54 PM
Last modification on : Tuesday, July 20, 2021 - 3:03:46 AM
Long-term archiving on: : Saturday, September 12, 2015 - 6:35:19 AM
Contributor : Marc Schoenauer Connect in order to contact the contributor
Submitted on : Tuesday, January 27, 2015 - 6:44:54 PM
Last modification on : Tuesday, July 20, 2021 - 3:03:46 AM
Long-term archiving on: : Saturday, September 12, 2015 - 6:35:19 AM
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dalcheSebagECML2014-author.pdf
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