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PKDD
2015
Springer

Safe Exploration for Active Learning with Gaussian Processes

3 years 10 months ago
Safe Exploration for Active Learning with Gaussian Processes
In this paper, the problem of safe exploration in the active learning context is considered. Safe exploration is especially important for data sampling from technical and industrial systems, e.g. combustion engines and gas turbines, where critical and unsafe measurements need to be avoided. The objective is to learn data-based regression models from such technical systems using a limited budget of measured, i.e. labelled, points while ensuring that critical regions of the considered systems are avoided during measurements. We propose an approach for learning such models and exploring new data regions based on Gaussian processes (GP’s). In particular, we employ a problem specific GP classifier to identify safe and unsafe regions, while using a differential entropy criterion for exploring relevant data regions. A theoretical analysis is shown for the proposed algorithm, where we provide an upper bound for the probability of failure. To demonstrate the efficiency and robustness of ou...
Jens Schreiter, Duy Nguyen-Tuong, Mona Eberts, Bas
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PKDD
Authors Jens Schreiter, Duy Nguyen-Tuong, Mona Eberts, Bastian Bischoff, Heiner Markert, Marc Toussaint
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