We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with large class clustering objectives, including prototype or graph based. Fundamentally different from the existing combinatorial and spectral solutions, our multi-term approach enables to control trade-off levels between objectives. derive tight upper bound based on...

We model the societal task of redistricting political districts as a partitioning problem: Given set n points in plane, each belonging to one two parties, and parameter k, our goal is compute partition P plane into regions so that region contains roughly s = n/k points. should satisfy notion "local" fairness, which related core, well-studied concept cooperative game theory. A associated with ma...

Tabular format is a common in open data. However, the meaning of columns not always explicit which makes if difficult for non-domain experts to reuse While most efforts making data FAIR are limited semantic metadata describing overall features datasets, such description enough ensure interoperability and reusability. This paper proposes reduce this weakness thanks (FAIR) core model that able re...