Clustering algorithms are a useful tool to explore data structures and have been employed
in many disciplines. The focus of this paper is the partitioning clustering problem with a …

We survey unsupervised machine learning algorithms in the context of outlier detection.
This task challenges state-of-the-art methods from a variety of research fields to applications …

The composition of multiple Gaussian Processes as a Deep Gaussian Process DGP enables
a deep probabilistic nonparametric approach to flexibly tackle complex machine learning …

Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable
research effort has been made into attacking three issues with GP models: how to …

The analysis of large-scale Boolean network dynamics is of great importance in understanding
complex phenomena where systems are characterized by a large number of components…

Parameter inference in mechanistic models based on systems of coupled differential
equations is a topical yet computationally challenging problem, due to the need to follow each …

Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long
term pathological trajectories from short term clinical data. Along with the ability of providing a …

Modern password guessing attacks adopt sophisticated probabilistic techniques that allow
for orders of magnitude less guesses to succeed compared to brute force. Unfortunately, best …

The Bayesian treatment of neural networks dictates that a prior distribution is specified over
their weight and bias parameters. This poses a challenge because modern neural networks …

The main challenges that arise when adopting Gaussian process priors in probabilistic
modeling are how to carry out exact Bayesian inference and how to account for uncertainty on …