9 citations · 9 across the 2 of their papers we have counts for
5 papers
Vector-valued statistics of binomial processes: Berry-Esseen bounds in the convex distance
Mikołaj J. Kasprzak, Giovanni Peccati
We study the discrepancy between the distribution of a vector-valued functional of i.i.d. random elements and that of a Gaussian vector. Our main contribution is an explicit bound…
Stein's method of exchangeable pairs in multivariate functional approximations
Christian Döbler, Mikołaj J. Kasprzak
In this paper we develop a framework for multivariate functional approximation by a suitable Gaussian process via an exchangeable pairs coupling that satisfies a suitable approxima…
Validated Variational Inference via Practical Posterior Error Bounds
Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell +1
Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to th…
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak +1
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is…
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak +1
Gaussian processes (GPs) offer a flexible class of priors for nonparametric Bayesian regression, but popular GP posterior inference methods are typically prohibitively slow or lack…