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/

Zendy Plus

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

Zendy Tools

Zendy Open

State of the art online learning procedures focus either on selecting thebest alternative ("best arm identification") or on minimizing the cost (the"regret"). We merge these two objectives by providing the theoretical analysisof cost minimizing algorithms that are also delta-PAC (with a proven guaranteedbound on the decision time), hence fulfilling at the same time regretminimization and best arm identification. This analysis sheds light on thecommon observation that ill-callibrated UCB-algorithms minimize regret whilestill identifying quickly the best arm.  We also extend these results to the non-iid case faced by many practitioners.This provides a technique to make cost versus decision time compromise whendoing adaptive tests with applications ranging from website A/B testing toclinical trials.

Bridging the gap between regret minimization and best arm identification, with application to A/B tests