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• W3164693099 abstract "The ever-growing size of the datasets renders well-studied learning techniques, such as Kernel Ridge Regression, inapplicable, posing a serious computational challenge. Divide-and-conquer is a common remedy, suggesting to split the dataset into disjoint partitions, obtain the local estimates and average them, it allows to scale-up an otherwise ineffective base approach. In the current study we suggest a fully data-driven approach to quantify uncertainty of the averaged estimator. Namely, we construct simultaneous element-wise confidence bands for the predictions yielded by the averaged estimator on a given deterministic prediction set. The novel approach features rigorous theoretical guaranties for a wide class of base learners with Kernel Ridge regression being a special case. As a by-product of our analysis we also obtain a sup-norm consistency result for the divide-and-conquer Kernel Ridge Regression. The simulation study supports the theoretical findings." @default.
• W3164693099 created "2021-06-07" @default.
• W3164693099 creator A5040448099 @default.
• W3164693099 date "2021-05-24" @default.
• W3164693099 modified "2023-09-27" @default.
• W3164693099 title "Uncertainty quantification for distributed regression." @default.
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• W3164693099 hasPublicationYear "2021" @default.
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