Bayesian Active Learning with Fully Bayesian Gaussian Processes. (arXiv:2205.10186v1 [cs.LG])

The bias-variance trade-off is a well-known problem in machine learning that
only gets more pronounced the less available data there is. In active learning,
where labeled data is scarce or difficult to obtain, neglecting this trade-off
can cause inefficient and non-optimal querying, leading to unnecessary data
labeling. In this paper, we focus on active learning with Gaussian Processes
(GPs). For the GP, the bias-variance trade-off is made by optimization of the
two hyperparameters: the length scale and noise-term. Considering that …

active learning arxiv bayesian gaussian processes learning processes