Towards Large-scale Gaussian Process Models for Efficient Bayesian Machine Learning

Fabian Berns, Christian Beecks

2020

Abstract

Gaussian Process Models (GPMs) are applicable for a large variety of different data analysis tasks, such as time series interpolation, regression, and classification. Frequently, these models of bayesian machine learning instantiate a Gaussian Process by a zero-mean function and the well-known Gaussian kernel. While these default instantiations yield acceptable analytical quality for many use cases, GPM retrieval algorithms allow to automatically search for an application-specific model suitable for a particular dataset. State-of-the-art GPM retrieval algorithms have only been applied for small datasets, as their cubic runtime complexity impedes analyzing datasets beyond a few thousand data records. Even though global approximations of Gaussian Processes extend the applicability of those models to medium-sized datasets, sets of millions of data records are still far beyond their reach. Therefore, we develop a new large-scale GPM structure, which incorporates a divide-&-conquer-based paradigm and thus enables efficient GPM retrieval for large-scale data. We outline challenges concerning this newly developed GPM structure regarding its algorithmic retrieval, its integration with given data platforms and technologies, as well as cross-model comparability and interpretability.

Paper Citation

in Harvard Style

Berns F. and Beecks C. (2020). Towards Large-scale Gaussian Process Models for Efficient Bayesian Machine Learning.In Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-440-4, pages 275-282. DOI: 10.5220/0009874702750282

in Bibtex Style

@conference{data20,
author={Fabian Berns and Christian Beecks},
title={Towards Large-scale Gaussian Process Models for Efficient Bayesian Machine Learning},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2020},
pages={275-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009874702750282},
isbn={978-989-758-440-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Towards Large-scale Gaussian Process Models for Efficient Bayesian Machine Learning
SN - 978-989-758-440-4
AU - Berns F.
AU - Beecks C.
PY - 2020
SP - 275
EP - 282
DO - 10.5220/0009874702750282