Skip to yearly menu bar Skip to main content


Efficient Interpolation of Density Estimators

Paxton Turner · Jingbo Liu · Philippe Rigollet


Keywords: [ Learning Theory and Statistics ] [ Statistical Learning Theory ]


We study the problem of space and time efficient evaluation of a nonparametric estimator that approximates an unknown density. In the regime where consistent estimation is possible, we use a piecewise multivariate polynomial interpolation scheme to give a computationally efficient construction that converts the original estimator to a new estimator that can be queried efficiently and has low space requirements, all without adversely deteriorating the original approximation quality. Our result gives a new statistical perspective on the problem of fast evaluation of kernel density estimators in the presence of underlying smoothness. As a corollary, we give a succinct derivation of a classical result of Kolmogorov---Tikhomirov on the metric entropy of Holder classes of smooth functions.

Chat is not available.