English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Bayesian optimization usually assumes that a Bayesian prior is given.However, the strong theoretical guarantees in Bayesian optimization are oftenregrettably compromised in practice because of unknown parameters in the prior.In this paper, we adopt a variant of empirical Bayes and show that, byestimating the Gaussian process prior from offline data sampled from the sameprior and constructing unbiased estimators of the posterior, variants of bothGP-UCB and probability of improvement achieve a near-zero regret bound, whichdecreases to a constant proportional to the observational noise as the numberof offline data and the number of online evaluations increase. Empirically, wehave verified our approach on challenging simulated robotic problems featuringtask and motion planning.

Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior