# Learning-augmented count-min sketches via Bayesian nonparametrics

@article{Dolera2021LearningaugmentedCS, title={Learning-augmented count-min sketches via Bayesian nonparametrics}, author={Emanuele Dolera and Stefano Favaro and Stefano Peluchetti}, journal={ArXiv}, year={2021}, volume={abs/2102.04462} }

The count-min sketch (CMS) is a time and memory eﬃcient randomized data structure that provides estimates of tokens’ frequencies in a data stream of tokens, i.e. point queries, based on random hashed data. A learning-augmented version of the CMS, referred to as CMS-DP, has been proposed by Cai, Mitzenmacher and Adams ( NeurIPS 2018), and it relies on Bayesian nonparametric (BNP) modeling of the data stream of tokens via a Dirichlet process (DP) prior, with estimates of a point query being…

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