Reproduce “Socially Fair Clustering: Parameterized Approximation and Local Search” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Aditya Anand, Yury Makarychev, Liren Shan
The authors study socially fair clustering, where each point has weights for ℓ groups, and a shared set of centers is scored by the maximum group-specific weighted p-norm distance to a nearest center. They remove ℓ extra centers from a bicriteria solution using parameterized algorithms, obtaining a 17 + 6√6 + ε approximation in f(ℓ,p,1/ε)·n^O(1) time, a theoretical guarantee rather than measured improvement.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.