mlsanityData to 7 Oct 2026

On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters

arXiv 2112.06563 ↗First posted 13 Dec 2021cs.LG cs.NEcited by 4

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Abstract (arXiv)

Bloom Filters are a fundamental and pervasive data structure. Within the growing area of Learned Data Structures, several Learned versions of Bloom Filters have been considered, yielding advantages over classic Filters. Each of them uses a classifier, which is the Learned part of the data structure. Although it has a central role in those new filters, and its space footprint as well as classification time may affect the performance of the Learned Filter, no systematic study of which specific classifier to use in which circumstances is available. We report progress in this area here, providing also initial guidelines on which classifier to choose among five classic classification paradigms.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

10 of 37 references.

References · 10

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2017The Case for Learned Index Structurescited by 188
  3. 2020Algorithms with Predictionscited by 152
  4. 2019A Model for Learned Bloom Filters, and Optimizing by Sandwichingcited by 64
  5. 2020RadixSpline: A Single-Pass Learned Indexcited by 31
  6. 2020Benchmarking Learned Indexescited by 27
  7. 2019Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifiercited by 15
  8. 2020Partitioned Learned Bloom Filtercited by 10
  9. 2021A Critical Analysis of Recursive Model Indexescited by 9
  10. 2020Compression strategies and space-conscious representations for deep neural networkscited by 4

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2022On the Suitability of Neural Networks as Building Blocks for The Design of Efficient Learned Indexescited by 3
  2. 2022A Critical Analysis of Classifier Selection in Learned Bloom Filterscited by 0
  3. 2023From Specific to Generic Learned Sorted Set Dictionaries: A Theoretically Sound Paradigm Yelding Competitive Data Structural Boosters in Practicecited by 0
  4. 2025Cascaded Learned Bloom Filter for Optimizing Model-Filter Size Balance and Fast Rejectioncited by 0