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

In this paper, we study the combinatorial semi-bandits (CMAB) and focus onreducing the dependency of the batch-size $K$ in the regret bound, where $K$ isthe total number of arms that can be pulled or triggered in each round. First,for the setting of CMAB with probabilistically triggered arms (CMAB-T), wediscover a novel (directional) triggering probability and variance modulated(TPVM) condition that can replace the previously-used smoothness condition forvarious applications, such as cascading bandits, online network exploration andonline influence maximization. Under this new condition, we propose a BCUCB-Talgorithm with variance-aware confidence intervals and conduct regret analysiswhich reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regretbound, significantly improving the regret bounds for the above applications.Second, for the setting of non-triggering CMAB with independent arms, wepropose a SESCB algorithm which leverages on the non-triggering version of theTPVM condition and completely removes the dependency on $K$ in the leadingregret. As a valuable by-product, the regret analysis used in this paper canimprove several existing results by a factor of $O(\log K)$. Finally,experimental evaluations show our superior performance compared with benchmarkalgorithms in different applications.

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms