Efficient Algorithms for Fair and Diverse Subset Selection from High-Dimensional Data

The authors develop the first constant approximation algorithm for the Fair Max-Min Diversification (FairDiv) problem that runs in near-linear time using only linear space. Their approach employs a novel combination of the Multiplicative Weight Update method and advanced geometric data structures to implicitly and approximately solve a linear program.