# Compressing (Multidimensional) Learned Bloom Filters

arXiv: https://arxiv.org/abs/2208.03029 · HTML: https://mlsanity.com/paper/2208.03029/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 5 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [A Model for Learned Bloom Filters, and Optimizing by Sandwiching](https://mlsanity.com/paper/1901.00902/) (2019)
- [ALEX: An Updatable Adaptive Learned Index](https://mlsanity.com/paper/1905.08898/) (2019)
- [RadixSpline: A Single-Pass Learned Index](https://mlsanity.com/paper/2004.14541/) (2020)
- [Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads](https://mlsanity.com/paper/2006.13282/) (2020)
- [NeuroCard: One Cardinality Estimator for All Tables](https://mlsanity.com/paper/2006.08109/) (2020)
- [Partitioned Learned Bloom Filter](https://mlsanity.com/paper/2006.03176/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Survey of Learned Indexes for the Multi-dimensional Space](https://mlsanity.com/paper/2403.06456/) (2024)

## Abstract (arXiv)

Bloom filters are widely used data structures that compactly represent sets of elements. Querying a Bloom filter reveals if an element is not included in the underlying set or is included with a certain error rate. This membership testing can be modeled as a binary classification problem and solved through deep learning models, leading to what is called learned Bloom filters. We have identified that the benefits of learned Bloom filters are apparent only when considering a vast amount of data, and even then, there is a possibility to further reduce their memory consumption. For that reason, we introduce a lossless input compression technique that improves the memory consumption of the learned model while preserving a comparable model accuracy. We evaluate our approach and show significant memory consumption improvements over learned Bloom filters.