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BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
Summary: BlinkML enables fast, approximate ML training with probabilistic guarantees that the approximate model matches full-model predictions. Supports any MLE-based model (GLMs, PPCA) and uses error-bounded sampling to deliver 6x–629x speedups while preserving decisions.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
5670
Venue
SIGMOD
Year
2019
Pagerank
6.0892672e-05
Overall Rank
5,785 | 60.32%
DOI
10.1145/3299869.3300077
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{park_sigmod19,
title = {{BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees}},
author = {Park, Yongjoo and Qing, Jingyi and Shen, Xiaoyang and Mozafari, Barzan},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300077},
url = {https://dl.acm.org/doi/10.1145/3299869.3300077},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
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