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Sequential Model-Based Ensemble Optimization

by Alexandre Lacoste, Hugo Larochelle, Mario Marchand
"... One of the most tedious tasks in the applica-tion of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model-based optimization (SMBO) methods. This can be used to optimize a ..."
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One of the most tedious tasks in the applica-tion of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model-based optimization (SMBO) methods. This can be used to optimize

Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters

by Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
"... Abstract. Model selection and hyperparameter optimization is cru-cial in applying machine learning to a novel dataset. Recently, a sub-community of machine learning has focused on solving this prob-lem with Sequential Model-based Bayesian Optimization (SMBO), demonstrating substantial successes in m ..."
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propose to initialize SMBO with a small number of config-urations suggested by a metalearning procedure. The resulting simple MI-SMBO technique can be trivially applied to any SMBO method, allowing us to perform experiments on two quite different SMBO methods with complementary strengths applied

Hyperparameter Search Space Pruning A New Component for Sequential Model-Based Hyperparameter Optimization

by Martin Wistuba, Nicolas Schilling, Lars Schmidt-thieme
"... Abstract. The optimization of hyperparameters is often done manu-ally or exhaustively but recent work has shown that automatic methods can optimize hyperparameters faster and even achieve better final per-formance. Sequential model-based optimization (SMBO) is the current state of the art framework ..."
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Abstract. The optimization of hyperparameters is often done manu-ally or exhaustively but recent work has shown that automatic methods can optimize hyperparameters faster and even achieve better final per-formance. Sequential model-based optimization (SMBO) is the current state of the art framework

Learning Data Set Similarities for Hyperparameter Optimization Initializations

by Martin Wistuba, Nicolas Schilling, Lars Schmidt-thieme
"... Abstract. Current research has introduced new automatic hyperpa-rameter optimization strategies that are able to accelerate this opti-mization process and outperform manual and grid or random search in terms of time and prediction accuracy. Currently, meta-learning methods that transfer knowledge fr ..."
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such that we are able to profit from this in future evaluations. We empirically compare the distance function by applying it in the scenario of the initialization of SMBO by meta-learning. Our two proposed approaches are compared against three competitor meth-ods on one meta-data set with respect
Results 1 - 4 of 4