1 citations · 1 across the 3 of their papers we have counts for
4 papers
Adaptive sparse variational approximations for Gaussian process regression
Dennis Nieman, Botond Szabó
Accurate tuning of hyperparameters is crucial to ensure that models can generalise effectively across different settings. In this paper, we present theoretical guarantees for hyper…
Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression
Dennis Nieman, Botond Szabo, Harry van Zanten
We investigate the frequentist guarantees of the variational sparse Gaussian process regression model. In the theoretical analysis, we focus on the variational approach with spectr…
Input estimation from discrete workload observations in a Lévy-driven storage system
Dennis Nieman, Michel Mandjes, Liron Ravner
Our goal is to estimate the characteristic exponent of the input to a Lévy-driven storage system from a sample of equispaced workload observations. The estimator relies on an appro…
Contraction rates for sparse variational approximations in Gaussian process regression
Dennis Nieman, Botond Szabo, Harry van Zanten
We study the theoretical properties of a variational Bayes method in the Gaussian Process regression model. We consider the inducing variables method introduced by Titsias (2009a)…