skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Vassilvitskii, S"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ( , ICML)
    Free, publicly-accessible full text available November 1, 2024
  2. ; ; ; ( , International Conference on Machine Learning)
  3. ; ; ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  4. ; ; ( , International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; ( , Advances in neural information processing systems)
    null (Ed.)
    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. 
    more » « less
    Accepted Manuscript Full Text Available