Quality and Efficiency in Kernel Density Estimates for Large Data
Summary: Randomized and deterministic KDE algorithms with quality guarantees for huge data; no kernel or bandwidth knowledge required. Highly parallelizable, MapReduce-friendly; orders-of-magnitude efficiency gains with strong empirical validation on real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yan Zheng (University of Utah)
- 2. Jeffrey Jestes (University of Utah)
- 3. Jeff M. Phillips (University of Utah)
- 4. Feifei Li (University of Utah)
BibTeX Citation
@inproceedings{zheng_sigmod13,
title = {{Quality and Efficiency in Kernel Density Estimates for Large Data}},
author = {Zheng, Yan and Jestes, Jeffrey and Phillips, Jeff M. and Li, Feifei},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2465319},
url = {https://dl.acm.org/doi/10.1145/2463676.2465319},
year = {2013}
}
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