3 citations · 6 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
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…