30 citations · 111 across the 14 of their papers we have counts for
19 papers
Optimal testing using combined test statistics across independent studies
Botond Szabó, Aad van der Vaart, Lasse Vuursteen +1
Combining test statistics from independent trials or experiments is a popular method of meta-analysis. However, there is very limited theoretical understanding of the power of the…
Semi-parametric Bernstein-von Mises in Linear Inverse Problems
Adel Magra, Aad van der Vaart, Harry van Zanten
We consider a Bayesian approach for the recovery of scalar parameters arising in inverse problems. We consider a general signal-in white noise model where we have access to two ind…
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…
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case
Luis Pedro Silvestrin, Harry van Zanten, Mark Hoogendoorn +1
With the development of new sensors and monitoring devices, more sources of data become available to be used as inputs for machine learning models. These can on the one hand help t…
Optimal high-dimensional and nonparametric distributed testing under communication constraints
Botond Szabó, Lasse Vuursteen, Harry van Zanten
We derive minimax testing errors in a distributed framework where the data is split over multiple machines and their communication to a central machine is limited to bits. We i…
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)…
Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communication
Botond Szabo, Lasse Vuursteen, Harry van Zanten
In this paper we study the problem of signal detection in Gaussian noise in a distributed setting where the local machines in the star topology can communicate a single bit of info…
Minimax lower bounds for function estimation on graphs
Alisa Kirichenko, Harry van Zanten
We study minimax lower bounds for function estimation problems on large graph when the target function is smoothly varying over the graph. We derive minimax rates in the context of…
Nonparametric Bayesian label prediction on a graph
Jarno Hartog, Harry van Zanten
An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described. A hierarchical Bayesian approach with a randomly scaled Gau…
Full adaptation to smoothness using randomly truncated series priors with Gaussian coefficients and inverse gamma scaling
Jan van Waaij, Harry van Zanten
We study random series priors for estimating a functional parameter (f\in L^2[0,1]). We show that with a series prior with random truncation, Gaussian coefficients, and inverse gam…
Estimating a smooth function on a large graph by Bayesian Laplacian regularisation
Alisa Kirichenko, Harry van Zanten
We study a Bayesian approach to estimating a smooth function in the context of regression or classification problems on large graphs. We derive theoretical results that show how as…
Gaussian process methods for one-dimensional diffusions: optimal rates and adaptation
Jan van Waaij, Harry van Zanten
We study the performance of nonparametric Bayes procedures for one-dimensional diffusions with periodic drift. We improve existing convergence rate results for Gaussian process (GP…
Optimality of Poisson processes intensity learning with Gaussian processes
Alisa Kirichenko, Harry van Zanten
In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a -dime…
Honest Bayesian confidence sets for the L2-norm
Botond Szabo, Aad van der Vaart, Harry van Zanten
We investigate the problem of constructing Bayesian credible sets that are honest and adaptive for the L2-loss over a scale of Sobolev classes with regularity ranging between [D; 2…
Rate-optimal Bayesian intensity smoothing for inhomogeneous Poisson processes
Eduard Belitser, Paulo Serra, Harry van Zanten
We apply nonparametric Bayesian methods to study the problem of estimating the intensity function of an inhomogeneous Poisson process. We exhibit a prior on intensities which both…
Nonparametric Bayesian methods for one-dimensional diffusion models
Harry van Zanten
In this paper we review recently developed methods for nonparametric Bayesian inference for one-dimensional diffusion models. We discuss different possible prior distributions, com…
Nonparametric methods for volatility density estimation
Bert van Es, Peter Spreij, Harry van Zanten
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and re…
Nonparametric volatility density estimation for discrete time models
Bert van Es, Peter Spreij, Harry van Zanten
We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time i…
Nonparametric Volatility Density Estimation
Bert van Es, Peter Spreij, Harry van Zanten
We consider two kinds of stochastic volatility models. Both kinds of models contain a stationary volatility process, the density of which, at a fixed instant in time, we aim to est…