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Conference Papers Year : 2018

AutoML with Monte Carlo Tree Search

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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Dates and versions

hal-01966957 , version 1 (30-12-2018)

Identifiers

  • HAL Id : hal-01966957 , version 1

Cite

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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