Transfer operators such as the Perron–Frobenius or Koopman operator play an important
role in the global analysis of complex dynamical systems. The eigenfunctions of these …

We present a new operator-free, measure-theoretic approach to the conditional mean
embedding as a random variable taking values in a reproducing kernel Hilbert space. While …

We propose and analyze a novel theoretical and algorithmic framework for structured
prediction. While so far the term has referred to discrete output spaces, here we consider …

Conditional mean embeddings (CMEs) have proven themselves to be a powerful tool in
many machine learning applications. They allow the efficient conditioning of probability …

We introduce a novel conditional density estimationmodel termed the conditional
densityoperator (CDO). It naturally captures multivariate, multimodal output densities …

Given the joint distribution of two random variables $ X, Y $ on some second countable
locally compact Hausdorff space, we investigate the statistical approximation of the $ L^ 2 …

In the pharmaceutical industry, the transition to continuous manufacturing of solid dosage
forms is adopted by more and more companies. For these continuous processes, high …

Abstract Reproducing kernel Hilbert spaces (RKHSs) play an important role in many
statistics and machine learning applications ranging from support vector machines to …

We consider the problem of sequentially optimising the conditional expectation of an
objective function, with both the conditional distribution and the objective function assumed …

We propose a novel calibration method for computer simulators, dealing with the problem of
covariate shift. Covariate shift is the situation where input distributions for training and test …