no code implementations • NeurIPS 2021 • Zheyang Shen, Markus Heinonen, Samuel Kaski
Parallel to LD, Stein variational gradient descent (SVGD) similarly minimizes the KL, albeit endowed with a novel Stein-Wasserstein distance, by deterministically transporting a set of particle samples, thus de-randomizes the stochastic diffusion process.
no code implementations • 6 Mar 2020 • Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models.
no code implementations • 23 May 2019 • Zheyang Shen, Markus Heinonen, Samuel Kaski
We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions.
no code implementations • 10 Oct 2018 • Zheyang Shen, Markus Heinonen, Samuel Kaski
The expressive power of Gaussian processes depends heavily on the choice of kernel.