Thinking Critically About Fair Clustering: Past, Present, and Future

Fair clustering encompasses a diverse group of fundamental optimization problems spanning many subdomains from unsupervised learning in machine learning to facility location in operations research. This talk will provide a broad overview of common problems and algorithmic techniques in the fair clustering literature with a particular focus on k-clustering objectives (e.g., k-center, k-means). We will then discuss challenges and opportunities for growth in this nascent research area.

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