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Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to model the data set the Bloom filter is meant to represent.
A. Broder and M. Mitzenmacher. Network applications of bloom filters: A survey. Internet Mathematics
2004
Earlier work this paper cites.
M. Mitzenmacher and E. Upfal. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press. 2005
2005
Earlier work this paper cites.
K. Chung, M. Mitzenmacher, and S. Vadhan. Why Simple Hash Functions Work: Exploiting the Entropy in a Data Stream. Theory of Computing
2013
Earlier work this paper cites.
B. Fan, D. Andersen, M. Kaminsky, and M. Mitzenmacher. Cuckoo filter: Practically better than bloom. In Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies
2014
Cited alongside, same era.
M. Naor and E. Yogev. Bloom filters in adversarial environments. In CRYPTO 2015
2015
Cited alongside, same era.
https://arxiv.org/abs/1711.01616
M. Bender, M. Farach-Colton, M. Goswami, R. Johnson, S. McCauley, and S. Singh. Bloom Filters, Adaptivity, and the Dictionary Problem · 2017
Cited alongside, same era.
The Case for Learned Index Structures
T. Kraska, A. Beutel, E. H. Chi, J. Dean, and N. Polyzotis · 2017
Later among the works it cites.
M. Mitzenmacher, S. Pontarelli, and P. Reviriego. Adaptive Cuckoo Filters. In Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)
2018
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