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 address the issue of binary classification from positive and unlabeleddata (PU classification) with a selection bias in the positive data. During theobservation process, (i) a sample is exposed to a user, (ii) the user thenreturns the label for the exposed sample, and (iii) we however can only observethe positive samples. Therefore, the positive labels that we observe are acombination of both the exposure and the labeling, which creates a selectionbias problem for the observed positive samples. This scenario represents aconceptual framework for many practical applications, such as recommendersystems, which we refer to as ``learning from positive, unlabeled, and exposuredata'' (PUE classification). To tackle this problem, we initially assume accessto data with exposure labels. Then, we propose a method to identify thefunction of interest using a strong ignorability assumption and develop an``Automatic Debiased PUE'' (ADPUE) learning method. This algorithm directlydebiases the selection bias without requiring intermediate estimates, such asthe propensity score, which is necessary for other learning methods. Throughexperiments, we demonstrate that our approach outperforms traditional PUlearning methods on various semi-synthetic datasets.

Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data