140 citations · 149 across the 8 of their papers we have counts for
9 papers
Optimal recovery and uncertainty quantification for distributed Gaussian process regression
Amine Hadji, Tammo Hesselink, Botond Szabó
Gaussian Processes (GP) are widely used for probabilistic modeling and inference for nonparametric regression. However, their computational complexity scales cubicly with the sampl…
Uncertainty Quantification for nonparametric regression using Empirical Bayesian neural networks
Stefan Franssen, Botond Szabó
We propose a new, two-step empirical Bayes-type of approach for neural networks. We show in context of the nonparametric regression model that the procedure (up to a logarithmic fa…
Distributed Nonparametric Estimation under Communication Constraints
Azeem Zaman, Botond Szabó
In the era of big data, it is necessary to split extremely large data sets across multiple computing nodes and construct estimators using the distributed data. When designing distr…
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…
Can we trust Bayesian uncertainty quantification from Gaussian process priors with squared exponential covariance kernel?
Amine Hadji, Botond Szábo
We investigate the frequentist coverage properties of credible sets resulting in from Gaussian process priors with squared exponential covariance kernel. First we show that by sele…
Uncertainty quantification for the horseshoe
Stéphanie van der Pas, Botond Szabó, Aad van der Vaart
We investigate the credible sets and marginal credible intervals resulting from the horseshoe prior in the sparse multivariate normal means model. We do so in an adaptive setting w…
On Bayesian based adaptive confidence sets for linear functionals
Botond Szabó
We consider the problem of constructing Bayesian based confidence sets for linear functionals in the inverse Gaussian white noise model. We work with a scale of Gaussian priors ind…
A sharp adaptive confidence ball for self-similar functions
Richard Nickl, Botond Szabó
In the nonparametric Gaussian sequence space model an -confidence ball is constructed that adapts to unknown smoothness and Sobolev-norm of the infinite-dimensional p…
Frequentist coverage of adaptive nonparametric Bayesian credible sets
Botond Szabó, A. W. van der Vaart, J. H. van Zanten
We investigate the frequentist coverage of Bayesian credible sets in a nonparametric setting. We consider a scale of priors of varying regularity and choose the regularity by an em…