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 …

Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for
understanding mechanisms of cancer progression and immunotherapy response. We …

Land-cover map** in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics
experiments usually rely on tandem MS to identify the thousands of compounds in a …

The problem of cross-platform binary code similarity detection aims at detecting whether two
binary functions coming from different platforms are similar or not. It has many security …

Kernel classifiers and regressors designed for structured data, such as sequences, trees
and graphs, have significantly advanced a number of interdisciplinary areas such as …

Cross-modal retrieval methods build a common representation space for samples from
multiple modalities, typically from the vision and the language domains. For images and …

Diversity is an important criterion for many areas of machine learning (ML), including
generative modeling and dataset curation. Yet little work has gone into understanding …