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

Motivated by concerns about making online decisions that incur undue amountof risk at each time step, in this paper, we formulate the probablyanytime-safe stochastic combinatorial semi-bandits problem. In this problem,the agent is given the option to select a subset of size at most $K$ from a setof $L$ ground items. Each item is associated to a certain mean reward as wellas a variance that represents its risk. To mitigate the risk that the agentincurs, we require that with probability at least $1-\delta$, over the entirehorizon of time $T$, each of the choices that the agent makes should containitems whose sum of variances does not exceed a certain variance budget. We callthis probably anytime-safe constraint. Under this constraint, we design andanalyze an algorithm {\sc PASCombUCB} that minimizes the regret over thehorizon of time $T$. By developing accompanying information-theoretic lowerbounds, we show that under both the problem-dependent and problem-independentparadigms, {\sc PASCombUCB} is almost asymptotically optimal. Experiments areconducted to corroborate our theoretical findings. Our problem setup, theproposed {\sc PASCombUCB} algorithm, and novel analyses are applicable todomains such as recommendation systems and transportation in which an agent isallowed to choose multiple items at a single time step and wishes to controlthe risk over the whole time horizon.

Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits