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

In this paper, we analyze the continuous armed bandit problems for nonconvexcost functions under certain smoothness and sublevel set assumptions. We firstderive an upper bound on the expected cumulative regret of a simple binsplitting method. We then propose an adaptive bin splitting method, which cansignificantly improve the performance. Furthermore, a minimax lower bound isderived, which shows that our new adaptive method achieves locally minimaxoptimal expected cumulative regret.

Optimal Stochastic Nonconvex Optimization with Bandit Feedback