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Found 4 papers, 2 papers with code
Implications of Distance over Redistricting Maps: Central and Outlier Maps
Specifically, we define a central map which may be considered as being "most typical" and give a rigorous justification for it by showing that it mirrors the Kemeny ranking in a scenario where we have a committee voting over a collection of redistricting maps to be drawn.
A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications.
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research.
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Clustering is a foundational problem in machine learning with numerous applications.
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