mlsanityData to 7 Oct 2026

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier

arXiv 1910.09131 ↗First posted 21 Oct 2019cs.DS cs.LGcited by 15

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Abstract (arXiv)

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 proposed new algorithms that generalize the learned Bloom filter by using the complete spectrum of the scores regions. We proved our algorithms have lower False Positive Rate (FPR) and memory usage compared with the existing approaches to learned Bloom filter. We also demonstrated the improved performance of our algorithms on real-world datasets.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 1; 2024: 1; 2025: 3; 2026: 3.

15 papers.

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