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AutoML with Monte Carlo Tree Search

Herilalaina Rakotoarison 1, 2 Michèle Sebag 2, 1 
1 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
Abstract : The sensitivity of machine learning (ML) algorithms w.r.t. their hyper-parameters and the difficulty of finding the ML algorithm and hyper-parameter setting best suited to a given dataset has led to the rapidly developing field of automated machine learning (AutoML), at the crossroad of meta-learning and structured optimization. Several international AutoML challenges have been organized since 2015, motivating the development of the Bayesian optimization-based approach Auto-Sklearn (Feurer et al., 2015) and the Bandit-based approach Hyperband (Li et al., 2016). In this paper, a new approach, called Monte Carlo Tree Search for Algorithm Configuration (Mosaic), is presented, fully exploiting the tree structure of the algorithm portfolio and hyper-parameter search space. Experiments (on 133 datasets of the OpenML repository) show that Mosaic performances match that of Auto-Sklearn.
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Submitted on : Sunday, December 30, 2018 - 11:00:01 AM
Last modification on : Tuesday, October 25, 2022 - 4:16:36 PM
Long-term archiving on: : Sunday, March 31, 2019 - 12:37:28 PM


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  • HAL Id : hal-01966957, version 1


Herilalaina Rakotoarison, Michèle Sebag. AutoML with Monte Carlo Tree Search. Workshop AutoML 2018 @ ICML/IJCAI-ECAI, Pavel Brazdil, Christophe Giraud-Carrier, and Isabelle Guyon, Jul 2018, Stockholm, Sweden. ⟨hal-01966957⟩



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