Now showing items 1-7 of 7

    • A Bias-Variance-Privacy Trilemma for Statistical Estimation 

      Regehr, Matthew (University of Waterloo, 2023-08-28)
      The canonical algorithm for differentially private mean estimation is to first clip the samples to a bounded range and then add noise to their empirical mean. Clipping controls the sensitivity and, hence, the variance of ...
    • Identifying regions of trusted prediction 

      Ananthakrishnan, Nivasini (University of Waterloo, 2021-07-20)
      Quantifying the probability of a label prediction being correct on a given test point or a given sub-population enables users to better decide how to use and when to trust machine learning derived predictors. In this work, ...
    • On Computable Online Learning 

      Hasrati, Niki (University of Waterloo, 2023-04-27)
      We initiate a study of computable online (c-online) learning, which we analyze under varying requirements for "optimality" in terms of the mistake bound. Our main contribution is to give a necessary and sufficient condition ...
    • A PAC-Theory of Clustering with Advice 

      Zokaei Ashtiani, Mohammad (University of Waterloo, 2018-05-17)
      In the absence of domain knowledge, clustering is usually an under-specified task. For any clustering application, one can choose among a variety of different clustering algorithms, along with different preprocessing ...
    • Private Distribution Learning with Public Data 

      Bie, Alex (University of Waterloo, 2024-01-22)
      We study the problem of private distribution learning with access to public data. In this setup, a learner is given both public and private samples drawn from an unknown distribution 𝑝 belonging to a class 𝑄, and has the ...
    • Theoretical foundations for efficient clustering 

      Kushagra, Shrinu (University of Waterloo, 2019-06-07)
      Clustering aims to group together data instances which are similar while simultaneously separating the dissimilar instances. The task of clustering is challenging due to many factors. The most well-studied is the high ...
    • Trade-Offs between Fairness, Interpretability, and Privacy in Machine Learning 

      Agarwal, Sushant (University of Waterloo, 2020-05-14)
      Algorithms have increasingly been deployed to make consequential decisions, and there have been many ethical questions raised about how these algorithms function. Three ethical considerations we look at in this work are ...


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