Deep Unsupervised Cardinality Estimation
Summary: Deep autoregressive models for unsupervised cardinality estimation, no independence assumptions. Monte Carlo integration over autoregressors enables range queries across dimensions, delivering single-digit tail error and up to 90x gains vs baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zongheng Yang (University of California Berkeley)
- 2. Eric Liang (University of California Berkeley)
- 3. Amog Kamsetty (University of California Berkeley)
- 4. Chenggang Wu (University of California Berkeley)
- 5. Yan Duan (Covariant)
- 6. Xi Chen (Covariant; University of California Berkeley)
- 7. Pieter Abbeel (Covariant; University of California Berkeley)
- 8. Joseph M. Hellerstein (University of California Berkeley)
- 9. Sanjay Krishnan (University of Chicago)
- 10. Ion Stoica (University of California Berkeley)
BibTeX Citation
@article{yang_vldb20,
title = {{Deep Unsupervised Cardinality Estimation}},
author = {Yang, Zongheng and Liang, Eric and Kamsetty, Amog and Wu, Chenggang and Duan, Yan and Chen, Xi and Abbeel, Pieter and Hellerstein, Joseph M. and Krishnan, Sanjay and Stoica, Ion},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {3},
pages = {279--292},
doi = {10.14778/3368289.3368294},
url = {https://doi.org/10.14778/3368289.3368294},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 50 of 103 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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