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Creators/Authors contains: "Duchi, John"

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  1. ; ( , The Annals of Statistics)
    Free, publicly-accessible full text available February 1, 2025
  2. ; ; ( , International Conference on Learning Theory)
    Free, publicly-accessible full text available June 1, 2024
  3. ; ; ( , Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  4. ; ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  5. ; ; ( , Operations Research)
    While modern large-scale data sets often consist of heterogeneous subpopulations—for example, multiple demographic groups or multiple text corpora—the standard practice of minimizing average loss fails to guarantee uniformly low losses across all subpopulations. We propose a convex procedure that controls the worst case performance over all subpopulations of a given size. Our procedure comes with finite-sample (nonparametric) convergence guarantees on the worst-off subpopulation. Empirically, we observe on lexical similarity, wine quality, and recidivism prediction tasks that our worst case procedure learns models that do well against unseen subpopulations. 
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  6. ; ; ; ; ( , The Annals of Statistics)
    Full Text Available 
  7. ; ; ( , Conference on Learning Theory)
    Accepted Manuscript Full Text Available
  8. ; ; ( , Proceedings of Machine Learning Research)
  9. ; ; ; ( , Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  10. ; ; ; ( , Proceedings of Machine Learning Research)