Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity
Summary: Entropy-Learned Hashing models input entropy to build data-specific hash functions, cutting unnecessary randomness extraction and computation while preserving uniform output. Evaluations on hash tables, Bloom filters, and partitioning show 3.7–14x throughput gains over best-in-class implementations. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Brian Hentschel (Harvard University)
- 2. Utku Sirin (Harvard University)
- 3. Stratos Idreos (Harvard University)
BibTeX Citation
@inproceedings{hentschel_sigmod22,
title = {{Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity}},
author = {Hentschel, Brian and Sirin, Utku and Idreos, Stratos},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517894},
url = {https://dl.acm.org/doi/10.1145/3514221.3517894},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,025 | VIP Hashing - Adapting to Skew in Popularity of Data on the Fly | 2022 | VLDB | 5.1757914e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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