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Automated Machine Learning with Monte-Carlo Tree Search

Herilalaina Rakotoarison 1, 2 Marc Schoenauer 1, 2 Michèle Sebag 1, 3, 2 
1 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
Abstract : The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand. MOSAIC, a Monte-Carlo tree search (MCTS) based approach, is presented to handle the AutoML hybrid structural and parametric expensive black-box optimization problem. Extensive empirical studies are conducted to independently assess and compare: i) the optimization processes based on Bayesian optimization or MCTS; ii) its warm-start initializa-tion; iii) the ensembling of the solutions gathered along the search. MOSAIC is assessed on the OpenML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over AUTO-SKLEARN, winner of former international AutoML challenges.
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Submitted on : Monday, September 30, 2019 - 10:12:16 AM
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Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag. Automated Machine Learning with Monte-Carlo Tree Search. IJCAI-19 - 28th International Joint Conference on Artificial Intelligence, Aug 2019, Macau, China. pp.3296-3303, ⟨10.24963/ijcai.2019/457⟩. ⟨hal-02300884⟩



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