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

Inference of community structure in probabilistic graphical models may not beconsistent with fairness constraints when nodes have demographic attributes.Certain demographics may be over-represented in some detected communities andunder-represented in others. This paper defines a novel $\ell_1$-regularizedpseudo-likelihood approach for fair graphical model selection. In particular,we assume there is some community or clustering structure in the trueunderlying graph, and we seek to learn a sparse undirected graph and itscommunities from the data such that demographic groups are fairly representedwithin the communities. In the case when the graph is known a priori, weprovide a convex semidefinite programming approach for fair communitydetection. We establish the statistical consistency of the proposed method forboth a Gaussian graphical model and an Ising model for, respectively,continuous and binary data, proving that our method can recover the graphs andtheir fair communities with high probability.

Fair Community Detection and Structure Learning in Heterogeneous Graphical Models