NeurIPS 2020poster36 citations
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web
Zhenwei Dai, Anshumali Shrivastava
Abstract
Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier. However, such learned Bloom filter does not take full advantage of the predicted probability scores. We propose new algorithms that generalize the learned Bloom filter by using the complete spectrum of the score regions. We prove our algorithms have lower false positive rate (FPR) and memory usage compared with the existing approaches to learned Bloom filter. We also demonstrate the improved performance of our algorithms on real-world information filtering tasks over the web.
BibTeX
@inproceedings{NEURIPS2020_86b94dae,
author = {Dai, Zhenwei and Shrivastava, Anshumali},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {11700--11710},
publisher = {Curran Associates, Inc.},
title = {Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/86b94dae7c6517ec1ac767fd2c136580-Paper.pdf},
volume = {33},
year = {2020}
}