# Learning Filters with Certainty

arXiv: https://arxiv.org/abs/2606.22786 · HTML: https://mlsanity.com/paper/2606.22786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [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)
- [Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier](https://mlsanity.com/paper/1910.09131/) (2019)

## Abstract (arXiv)

Hash-based data structures such as Bloom filters are widely used in network systems for tasks including caching, anomaly detection, and machine learning pipelines. They typically provide binary indications of whether an element belongs to a set of interest, e.g., the contents of a cache. When uncertainty arises due to hash collisions, a positive indication is returned to avoid false negatives. We argue that the certainty associated with such indications can itself be useful information. This work focuses on Counting Bloom Filters (CBFs), a Bloom-filter variant that maintains counters rather than bits. Besides supporting insertions and deletions, these counters provide additional information that can be used to estimate the certainty of positive membership indications. We show how this certainty signal can be exploited in architectures that combine Bloom Filters with machine learning (ML) models.