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

SafePredict is a novel meta-algorithm that works with any base predictionalgorithm for online data to guarantee an arbitrarily chosen correctness rate,$1-\epsilon$, by allowing refusals. Allowing refusals means that themeta-algorithm may refuse to emit a prediction produced by the base algorithmon occasion so that the error rate on non-refused predictions does not exceed$\epsilon$. The SafePredict error bound does not rely on any assumptions on thedata distribution or the base predictor. When the base predictor happens not toexceed the target error rate $\epsilon$, SafePredict refuses only a finitenumber of times. When the error rate of the base predictor changes through timeSafePredict makes use of a weight-shifting heuristic that adapts to thesechanges without knowing when the changes occur yet still maintains thecorrectness guarantee. Empirical results show that (i) SafePredict comparesfavorably with state-of-the art confidence based refusal mechanisms which failto offer robust error guarantees; and (ii) combining SafePredict with suchrefusal mechanisms can in many cases further reduce the number of refusals. Oursoftware (currently in Python) is included in the supplementary material.

SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness