# Cascaded Learned Bloom Filter for Optimizing Model-Filter Size Balance and Fast Rejection

arXiv: https://arxiv.org/abs/2502.03696 · HTML: https://mlsanity.com/paper/2502.03696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [A Model for Learned Bloom Filters, and Optimizing by Sandwiching](https://mlsanity.com/paper/1901.00902/) (2019)
- [Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier](https://mlsanity.com/paper/1910.09131/) (2019)
- [On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters](https://mlsanity.com/paper/2112.06563/) (2021)
- [Ribbon filter: practically smaller than Bloom and Xor](https://mlsanity.com/paper/2103.02515/) (2021)
- [Using cascading Bloom filters to improve the memory usage for de Brujin graphs](https://mlsanity.com/paper/1302.7278/) (2013)
- [Hash Adaptive Bloom Filter](https://mlsanity.com/paper/2106.07037/) (2021)

## Abstract (arXiv)

Recent studies have demonstrated that learned Bloom filters (LBFs), which combine machine learning with the classical Bloom filter, can achieve superior memory efficiency. However, two challenges remain: (1) jointly optimizing the sizes of the machine learning model and Bloom filters, and (2) systematically minimizing reject time. We propose the Cascaded Learned Bloom Filter (CLBF), a unified architecture that generalizes existing LBF designs, including Sandwiched LBF and Partitioned LBF. Within this framework, we develop a dynamic programming-based optimizer that explores a discretized parameter space and identifies near-optimal configurations that balance model and filter sizes while achieving fast rejection. Experiments on real-world datasets show that CLBF reduces memory usage by up to 17% and decreases reject time by up to a factor of 65 compared to Partitioned LBF, the state-of-the-art LBF in terms of memory efficiency under a fixed machine learning model. Our code is publicly available at https://github.com/atsukisato/CascadedLBF.