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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Automating statistical modelling is a challenging problem in artificialintelligence. The Automatic Statistician takes a first step in this direction,by employing a kernel search algorithm with Gaussian Processes (GP) to provideinterpretable statistical models for regression problems. However this does notscale due to its $O(N^3)$ running time for the model selection. We proposeScalable Kernel Composition (SKC), a scalable kernel search algorithm thatextends the Automatic Statistician to bigger data sets. In doing so, we derivea cheap upper bound on the GP marginal likelihood that sandwiches the marginallikelihood with the variational lower bound . We show that the upper bound issignificantly tighter than the lower bound and thus useful for model selection.

Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes