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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.


, Student, University of Utah