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Fair Hierarchical Clustering
Sara Ahmadian · Alessandro Epasto · Marina Knittel · Ravi Kumar · Mohammad Mahdian · Benjamin Moseley · Philip Pham · Sergei Vassilvitskii · Yuyan Wang

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #859

As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering.

In this paper we extend this notion to hierarchical clustering, where the goal is to recursively partition the data to optimize a specific objective. For various natural objectives, we obtain simple, efficient algorithms to find a provably good fair hierarchical clustering. Empirically, we show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.

Author Information

Sara Ahmadian (Google Research)
Alessandro Epasto (Google)

I am a senior research scientist at Google, New York working in the Google Research Algorithms and Optimization team lead by Vahab Mirrokni. I received a Ph.D in computer science from Sapienza University of Rome, where I was advised by Professor Alessandro Panconesi and supported by the Google Europe Ph.D. Fellowship in Algorithms, 2011. I was also a post-doc at the department of computer science of Brown University in Providence (RI), USA where I was advised by Professor Eli Upfal. My research interests include algorithmic problems in machine learning and data mining, in particular in the areas of clustering, and large scale graphs analysis.

Marina Knittel (University of Maryland, College Park)
Ravi Kumar (Google)
Mohammad Mahdian (Google Research)
Benjamin Moseley (Carnegie Mellon University)
Philip Pham (Google)
Sergei Vassilvitskii (Google)
Yuyan Wang (Carnegie Mellon University)

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