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

Distributional data Shapley value (DShapley) has recently been proposed as aprincipled framework to quantify the contribution of individual datum inmachine learning. DShapley develops the foundational game theory concept ofShapley values into a statistical framework and can be applied to identify datapoints that are useful (or harmful) to a learning algorithm. EstimatingDShapley is computationally expensive, however, and this can be a majorchallenge to using it in practice. Moreover, there has been little mathematicalanalyses of how this value depends on data characteristics. In this paper, wederive the first analytic expressions for DShapley for the canonical problemsof linear regression, binary classification, and non-parametric densityestimation. These analytic forms provide new algorithms to estimate DShapleythat are several orders of magnitude faster than previous state-of-the-artmethods. Furthermore, our formulas are directly interpretable and providequantitative insights into how the value varies for different types of data. Wedemonstrate the practical efficacy of our approach on multiple real andsynthetic datasets.

Efficient computation and analysis of distributional Shapley values