Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

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 …

The topic of fake news has drawn attention both from the public and the academic
communities. Such misinformation has the potential of affecting public opinion, providing an …

Anomalies are occurrences in a dataset that are in some way unusual and do not fit the
general patterns. The concept of the anomaly is typically ill defined and perceived as vague …

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 …

Traditional anomaly detection on social media mostly focuses on individual point anomalies
while anomalous phenomena usually occur in groups. Therefore, it is valuable to study the …

Deep generative models have been widely used in practical applications such as the
detection of out-of-distribution (OOD) data. In this work, we aim to re-examine the potential of …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

Social platforms became a major source of rumours. While rumours can have severe real-
world implications, their detection is notoriously hard: Content on social platforms is short …

We introduce Isolation Distributional Kernel as a new way to measure the similarity between
two distributions. Existing approaches based on kernel mean embedding, which converts a …