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Scalable Meta-Bayesian Based Hyperparameters Optimization for Machine Learning

open access: yesCommunications in Computer and Information Science, 2022
International audienceIt is a known fact that the selection of one or more optimized algorithms and the configuration of significant hyperparameters, is among the major problems for the advanced data analytics using Machine Learning (ML) methodologies ...
Mohamed Hamlich   +2 more
exaly   +2 more sources

Reproducible Hyperparameter Optimization

Journal of Computational and Graphical Statistics, 2021
A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead t...
Lars Hertel   +2 more
openaire   +2 more sources

Hyperparameter Optimization Machines

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016
Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter
Martin Wistuba   +2 more
openaire   +1 more source

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