Data-driven confidence bands for distributed nonparametric regression
Authors
- Avanesov, Valeriy
2010 Mathematics Subject Classification
- 62G15 62F40 60G15
Keywords
- Gaussian process regression, kernel ridge regression, distributed regression, confidence bands, bootstrap
DOI
Abstract
Gaussian Process Regression and Kernel Ridge Regression are popular nonparametric regression approaches. Unfortunately, they suffer from high computational complexity rendering them inapplicable to the modern massive datasets. To that end a number of approximations have been suggested, some of them allowing for a distributed implementation. One of them is the divide and conquer approach, splitting the data into a number of partitions, obtaining the local estimates and finally averaging them. In this paper we suggest a novel computationally efficient fully data-driven algorithm, quantifying uncertainty of this method, yielding frequentist $L_2$-confidence bands. We rigorously demonstrate validity of the algorithm. Another contribution of the paper is a minimax-optimal high-probability bound for the averaged estimator, complementing and generalizing the known risk bounds.
Appeared in
- Proceedings of Thirty Third Conference on Learning Theory, J. Abernethy, S. Agarwal , eds., vol. 125 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 300--322.
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