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

We design simple screening tests to automatically discard data samples inempirical risk minimization without losing optimization guarantees. We deriveloss functions that produce dual objectives with a sparse solution. We alsoshow how to regularize convex losses to ensure such a dual sparsity-inducingproperty, and propose a general method to design screening tests forclassification or regression based on ellipsoidal approximations of the optimalset. In addition to producing computational gains, our approach also allows usto compress a dataset into a subset of representative points.

Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions