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

Being able to infer ground truth from the responses of multiple imperfectadvisors is a problem of crucial importance in many decision-makingapplications, such as lending, trading, investment, and crowd-sourcing. Inpractice, however, gathering answers from a set of advisors has a cost.Therefore, finding an advisor selection strategy that retrieves a reliableanswer and maximizes the overall utility is a challenging problem. To addressthis problem, we propose a novel strategy for optimally selecting a set ofadvisers in a sequential binary decision-making setting, where multipledecisions need to be made over time. Crucially, we assume no access to groundtruth and no prior knowledge about the reliability of advisers. Specifically,our approach considers how to simultaneously (1) select advisors by balancingthe advisors' costs and the value of making correct decisions, (2) learn thetrustworthiness of advisers dynamically without prior information by askingmultiple advisers, and (3) make optimal decisions without access to the groundtruth, improving this over time. We evaluate our algorithm through severalnumerical experiments. The results show that our approach outperforms two othermethods that combine state-of-the-art models.

MADDM: Multi-Advisor Dynamic Binary Decision-Making by Maximizing the Utility