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• W3126867710 abstract "We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements. The problem occurs in many applications, including biology, genomics, computer systems and linguistics. A line of research spanning the last decade resulted in algorithms that estimate the support up to ±en from a sample of size O(log2⁡(1/e)⋅n/log⁡n), where n is the data set size. Unfortunately, this bound is known to be tight, limiting further improvements to the complexity of this problem. In this paper we consider estimation algorithms augmented with a machine-learning-based predictor that, given any element, returns an estimation of its frequency. We show that if the predictor is correct up to a constant approximation factor, then the sample complexity can be reduced significantly, to  log⁡(1/e)⋅n1−Θ(1/log⁡(1/e)). We evaluate the proposed algorithms on a collection of data sets, using the neural-network based estimators from {Hsu et al, ICLR'19} as predictors. Our experiments demonstrate substantial (up to 3x) improvements in the estimation accuracy compared to the state of the art algorithm." @default.
• W3126867710 created "2021-02-15" @default.
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• W3126867710 date "2021-05-03" @default.
• W3126867710 modified "2023-09-26" @default.
• W3126867710 title "Learning-based Support Estimation in Sublinear Time" @default.
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