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

Discriminative Feature Feedback is a setting proposed by Dastupta et al.(2018), which provides a protocol for interactive learning based on featureexplanations that are provided by a human teacher. The features distinguishbetween the labels of pairs of possibly similar instances. That work has shownthat learning in this model can have considerable statistical and computationaladvantages over learning in standard label-based interactive learning models.  In this work, we provide new robust interactive learning algorithms for theDiscriminative Feature Feedback model, with mistake bounds that aresignificantly lower than those of previous robust algorithms for this setting.In the adversarial setting, we reduce the dependence on the number of protocolexceptions from quadratic to linear. In addition, we provide an algorithm for aslightly more restricted model, which obtains an even smaller mistake bound forlarge models with many exceptions.  In the stochastic setting, we provide the first algorithm that converges tothe exception rate with a polynomial sample complexity. Our algorithm andanalysis for the stochastic setting involve a new construction that we callFeature Influence, which may be of wider applicability.

Improved Robust Algorithms for Learning with Discriminative Feature Feedback