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 and extend an online learning framework known asContext-Attentive Bandit, motivated by various practical applications, frommedical diagnosis to dialog systems, where due to observation costs only asmall subset of a potentially large number of context variables can be observedat each iteration;however, the agent has a freedom to choose which variables toobserve. We derive a novel algorithm, called Context-Attentive ThompsonSampling (CATS), which builds upon the Linear Thompson Sampling approach,adapting it to Context-Attentive Bandit setting. We provide a theoreticalregret analysis and an extensive empirical evaluation demonstrating advantagesof the proposed approach over several baseline methods on a variety ofreal-life datasets

Double-Linear Thompson Sampling for Context-Attentive Bandits