Posts Tagged ‘Kernels’

Andrew Gordon Wilson and Hannes Nickisch:
Kernel Interpolation for Scalable Structured Gaussian Processes
ICML 2015

This paper was clearly one of my highlights at ICML and falls into the category of large-scale kernel machines, one of the trends at ICML. Wilson and Nickisch combine the advantages of inducing point and structure-exploiting (e.g., Kronecker/Toeplitz) approaches.

The key idea behind structured kernel interpolation is (more…)


Ali Rahimi and Ben Recht:
Random Features for Large Scale Kernel Machines
NIPS 2007

In this paper, the authors propose to map data to a low-dimensional Euclidean space, such that the inner product in this space is a close approximation of the inner product computed by a stationary (shift-invariant) kernel (in a potentially infinite-dimensional RKHS). The approach is based on Bochner’s theorem.

The central equation is this one: (more…)