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 …

A wide spectrum of design and decision problems, including parameter tuning, A/B testing
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …

Transfer learning enables the transfer of knowledge gained while learning to perform one
task (source) to a related but different task (target), hence addressing the expense of data …

Adversarial learning has been embedded into deep networks to learn disentangled and
transferable representations for domain adaptation. Existing adversarial domain adaptation …

Deep networks have been successfully applied to learn transferable features for adapting
models from a source domain to a different target domain. In this paper, we present joint …

Inference, prediction, and control of complex dynamical systems from time series is
important in many areas, including financial markets, power grid management, climate and …

Abstract Domain generalization aims to apply knowledge gained from multiple labeled
source domains to unseen target domains. The main difficulty comes from the dataset bias …

The remaining useful life (RUL) prediction provides an essential basis for improving
mechanical equipment reliability. In practical application, the variant of working conditions …

Topological data analysis (TDA) is an emerging mathematical concept for characterizing
shapes in complex data. In TDA, persistence diagrams are widely recognized as a useful …

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 …