Publications
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Diversity sampling is an implicit regularization for kernel methods KU Leuven
Kernel methods have achieved very good performance on large scale regression and classification problems by using the Nyström method and preconditioning techniques. The Nyström approximation---based on a subset of landmarks---gives a low rank approximation of the kernel matrix, and is known to provide a form of implicit regularization. We further elaborate on the impact of sampling diverse landmarks for constructing the Nyström approximation in ...
A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels KU Leuven
Random Fourier features (RFFs) have been successfully employed to kernel approximation in large-scale situations. The rationale behind RFF relies on Bochner's theorem, but the condition is too strict and excludes many widely used kernels, e.g., dot-product kernels (violates the shift-invariant condition) and indefinite kernels [violates the positive definite (PD) condition]. In this article, we present a unified RFF framework for indefinite ...