GPy-ABCD: A Configurable Automatic Bayesian Covariance Discovery Implementation

Thomas Fletcher, Alan Bundy, Kwabena Nuamah

Research output: Contribution to conferencePosterpeer-review

Abstract / Description of output

Gaussian Processes (GPs) are a very flexible class of nonparametric models which are able to fit data with very few assumptions, namely just the type of correlation (kernel) the data is expected to display. Automatic Bayesian Covariance Discovery (ABCD)1 is an iterative modular Gaussian Process regression framework aimed at removing the requirement for even this initial correlation form assumption. GPy-ABCD2 is a new implementation of an ABCD system built for ease of use and configurability; it can produce short text descriptions of fit models, it uses a revised model-space search algorithm and it removes a search bias which was required in order to retain model explainability in the original system.
Original languageEnglish
Number of pages1
Publication statusE-pub ahead of print - 23 Jul 2021
Event8th ICML Workshop on Automated Machine Learning: Collocated with the Thirty-eighth International Conference on Machine Learning (ICML) - Virtual
Duration: 23 Jul 202123 Jul 2021


Workshop8th ICML Workshop on Automated Machine Learning
Abbreviated titleAutoML
Internet address


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