Fair Polylog-Approximate Low-Cost Hierarchical Clustering

no code implementations • 21 Nov 2023 • Marina Knittel, Max Springer, John Dickerson, Mohammadtaghi Hajiaghayi

Research in fair machine learning, and particularly clustering, has been crucial in recent years given the many ethical controversies that modern intelligent systems have posed.

Clustering Fairness

Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost

no code implementations • 27 May 2022 • Marina Knittel, Max Springer, John P. Dickerson, Mohammadtaghi Hajiaghayi

We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation.

Clustering Fairness

The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations

no code implementations • 22 Feb 2022 • Marina Knittel, Samuel Dooley, John P. Dickerson

We also assume the agent's preferences over entire matchings are determined by a general weighted valuation function of their (and their affiliates') matches.

Fair Hierarchical Clustering

no code implementations • NeurIPS 2020 • Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.

BIG-bench Machine Learning Clustering +1