Stat Cafe - Abhisek Chakraborty

Probabilistic approaches for fair clustering

• Time: Monday 3/4/2024 from 11:40 AM to 12:30 PM
• Location: BLOC 448
• Pizza and drinks provided

Description

The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness. As clustering is one of the most commonly used unsupervised machine learning approaches, there has naturally been a proliferation of literature on fair clustering. Expanding upon the foundational work in Chierichetti et al. (2017), the literature has swiftly encompassed diverse extensions, primarily focusing on optimizing specific loss functions. In this talk, we discuss two distinct probabilistic approaches for conducting fair clustering, allowing uncertainty quantification, and complementing the existing optimization-based literature. Empirical success of the proposed methodology is demonstrated across varied numerical experiments, and benchmark data sets.

Presentation

Recording

Gallery