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 study the membership inference (MI) attack against classifiers, where the attacker's goal
is to determine whether a data instance was used for training the classifier. Through …

Transfer operators provide a rich framework for representing the dynamics of very general,
nonlinear dynamical systems. When interacting with reproducing kernel Hilbert spaces …

We consider the problem of learning a linear operator $\theta $ between two Hilbert spaces
from empirical observations, which we interpret as least squares regression in infinite …

Stochastic processes are random variables with values in some space of paths. However,
reducing a stochastic process to a path-valued random variable ignores its filtration, ie the …

Local permutation tests for conditional independence Page 1 The Annals of Statistics 2022, Vol.
50, No. 6, 3388–3414 https://doi.org/10.1214/22-AOS2233 © Institute of Mathematical Statistics …

We propose and study a class of simple, nonparametric, yet interpretable measures of
conditional dependence, which we call kernel partial correlation (KPC) coefficient, between …

We present a data-driven algorithm for efficiently computing stochastic control policies for
general joint chance constrained optimal control problems. Our approach leverages the …

Feature attribution for kernel methods is often heuristic and not individualised for each
prediction. To address this, we turn to the concept of Shapley values (SV), a coalition game …

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