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Tag: rates

  • Estimating Disease Rates Without Diagnosis

    Estimating Disease Rates Without Diagnosis Immune genes as predictors of disease The post Estimating Disease Rates Without Diagnosis appeared first on Towards Data Science. David Wells Go to original source

  • Rademacher learning rates for iterated random functions

    Rademacher learning rates for iterated random functions arXiv:2506.13946v1 Announce Type: new Abstract: Most existing literature on supervised machine learning assumes that the training dataset is drawn from an i.i.d. sample. However, many real-world problems exhibit temporal dependence and strong correlations between the marginal distributions of the data-generating process, suggesting that the i.i.d. assumption is often…

  • Minimax Rates for the Estimation of Eigenpairs of Weighted Laplace-Beltrami Operators on Manifolds

    Minimax Rates for the Estimation of Eigenpairs of Weighted Laplace-Beltrami Operators on Manifolds arXiv:2506.00171v1 Announce Type: new Abstract: We study the problem of estimating eigenpairs of elliptic differential operators from samples of a distribution $rho$ supported on a manifold $M$. The operators discussed in the paper are relevant in unsupervised learning and in particular are…

  • Minimax learning rates for estimating binary classifiers under margin conditions

    Minimax learning rates for estimating binary classifiers under margin conditions arXiv:2505.10628v1 Announce Type: new Abstract: We study classification problems using binary estimators where the decision boundary is described by horizon functions and where the data distribution satisfies a geometric margin condition. We establish upper and lower bounds for the minimax learning rate over broad function…

  • Universal Rates of Empirical Risk Minimization

    Universal Rates of Empirical Risk Minimization arXiv:2412.02810v1 Announce Type: new Abstract: The well-known empirical risk minimization (ERM) principle is the basis of many widely used machine learning algorithms, and plays an essential role in the classical PAC theory. A common description of a learning algorithm’s performance is its so-called “learning curve”, that is, the decay…