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.
Speaker(s)
, Student, University of Utah |