Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping. (arXiv:2206.03633v1 [cs.LG])

In machine learning, an agent needs to estimate uncertainty to efficiently
explore and adapt and to make effective decisions. A common approach to
uncertainty estimation maintains an ensemble of models. In recent years,
several approaches have been proposed for training ensembles, and conflicting
views prevail with regards to the importance of various ingredients of these
approaches. In this paper, we aim to address the benefits of two ingredients --
prior functions and bootstrapping -- which have come into question. We …

arxiv benefits bootstrapping lg prior uncertainty