A Model for Learned Bloom Filters and Optimizing by Sandwiching
Abstract
Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, with the following outcomes: (1) we clarify what guarantees can and cannot be associated with such a structure; (2) we show how to estimate what size the learning function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a learned Bloomier filter, based on our modeling approach.
Cite
Text
Mitzenmacher. "A Model for Learned Bloom Filters and Optimizing by Sandwiching." Neural Information Processing Systems, 2018.Markdown
[Mitzenmacher. "A Model for Learned Bloom Filters and Optimizing by Sandwiching." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/mitzenmacher2018neurips-model/)BibTeX
@inproceedings{mitzenmacher2018neurips-model,
title = {{A Model for Learned Bloom Filters and Optimizing by Sandwiching}},
author = {Mitzenmacher, Michael},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {464-473},
url = {https://mlanthology.org/neurips/2018/mitzenmacher2018neurips-model/}
}