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Quality and Efficiency in Kernel Density Estimates for Large Data

October 20, 2016
1:00 pm - 1:30 pm
GRB 310 A

Track: Data Science
Type: Presentation
Level: Advanced

Kernel density estimates are important for a broad variety of applications. Its construction has been well-studied, but existing techniques are expensive on massive datasets and/or only provide heuristic approximations without theoretical guarantees. We propose randomized and deterministic algorithms with quality guarantees which are orders of magnitude more efficient than previous algorithms.


Yan Zheng, Student, University of Utah