602 citations · 2k across the 59 of their papers we have counts for
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On Kernel Derivative Approximation with Random Fourier Features
Zoltan Szabo, Bharath K. Sriperumbudur
Random Fourier features (RFF) represent one of the most popular and wide-spread techniques in machine learning to scale up kernel algorithms. Despite the numerous successful applic…
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1
This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or infere…
Minimax Estimation of Quadratic Fourier Functionals
Shashank Singh, Bharath K. Sriperumbudur, Barnabás Póczos
We study estimation of (semi-)inner products between two nonparametric probability distributions, given IID samples from each distribution. These products include relatively well-s…