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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)

Paper ID
4726
Venue
SIGMOD
Year
2013
Pagerank
7.2381634e-05
Overall Rank
3,313 | 76.96%
DOI
-

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