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

A common approach to learning mobile health (mHealth) intervention policiesis linear Thompson sampling. Two desirable mHealth policy features are (1)pooling information across individuals and time and (2) incorporating atime-varying baseline reward. Previous approaches pooled information acrossindividuals but not time, failing to capture trends in treatment effects overtime. In addition, these approaches did not explicitly model the baselinereward, which limited the ability to precisely estimate the parameters in thedifferential reward model. In this paper, we propose a novel Thompson samplingalgorithm, termed ''DML-TS-NNR'' that leverages (1) nearest-neighbors toefficiently pool information on the differential reward function across usersand time and (2) the Double Machine Learning (DML) framework to explicitlymodel baseline rewards and stay agnostic to the supervised learning algorithmsused. By explicitly modeling baseline rewards, we obtain smaller confidencesets for the differential reward parameters. We offer theoretical guarantees onthe pseudo-regret, which are supported by empirical results. Importantly, theDML-TS-NNR algorithm demonstrates robustness to potential misspecifications inthe baseline reward model.

Debiased Machine Learning and Network Cohesion for Doubly-Robust Differential Reward Models in Contextual Bandits