A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

We study a class of dynamical systems modelled as stationary Markov chains that admit an
invariant distribution via the corresponding transfer or Koopman operator. While data-driven …

Instrumental variable (IV) regression is a strategy for learning causal relationships in
observational data. If measurements of input X and output Y are confounded, the causal …

We address the consistency of a kernel ridge regression estimate of the conditional mean
embedding (CME), which is an embedding of the conditional distribution of $ Y $ given $ X …

We address the problem of causal effect estima-tion in the presence of unobserved
confounding, but where proxies for the latent confounder (s) areobserved. We propose two …

Many modern applications of signal processing and machine learning, ranging from
computer vision to computational biology, require the analysis of large volumes of high …

A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central
to kernel methods in that it is used by many classical algorithms such as kernel principal …

Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer
learning, with a particular focus placed on domain adaptation from a theoretical point-of …

Applications of optimal transport have recently gained remarkable attention as a result of the
computational advantages of entropic regularization. However, in most situations the …

This paper studies the learning of linear operators between infinite-dimensional Hilbert
spaces. The training data comprises pairs of random input vectors in a Hilbert space and …