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

We analyze the regret of combinatorial Thompson sampling (CTS) for thecombinatorial multi-armed bandit with probabilistically triggered arms underthe semi-bandit feedback setting. We assume that the learner has access to anexact optimization oracle but does not know the expected base arm outcomesbeforehand. When the expected reward function is Lipschitz continuous in theexpected base arm outcomes, we derive $O(\sum_{i =1}^m \log T / (p_i\Delta_i))$ regret bound for CTS, where $m$ denotes the number of base arms,$p_i$ denotes the minimum non-zero triggering probability of base arm $i$ and$\Delta_i$ denotes the minimum suboptimality gap of base arm $i$. We alsocompare CTS with combinatorial upper confidence bound (CUCB) via numericalexperiments on a cascading bandit problem.

Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms