This tutorial introduces the reader to Gaussian process regression as an expressive tool to
model, actively explore and exploit unknown functions. Gaussian process regression is a …

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with
significant market potential. UAVs may lead to substantial cost savings in, for instance …

Example-based explanations are widely used in the effort to improve the interpretability of
highly complex distributions. However, prototypes alone are rarely sufficient to represent the …

We develop BatchBALD, a tractable approximation to the mutual information between a
batch of points and model parameters, which we use as an acquisition function to select …

We present a tutorial on Bayesian optimization, a method of finding the maximum of
expensive cost functions. Bayesian optimization employs the Bayesian technique of setting …

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over
time. This dynamic behavior is either caused by external factors or initiated by the system …

This paper studies domain generalization via domain-invariant representation learning.
Existing methods in this direction suppose that a domain can be characterized by styles of its …

Learning-based methods have been successful in solving complex control tasks without
significant prior knowledge about the system. However, these methods typically do not …

Submodularity1 is a property of set functions with deep theoretical consequences and far–
reaching applications. At first glance it appears very similar to concavity, in other ways it …

We propose a new objective function for superpixel segmentation. This objective function
consists of two components: entropy rate of a random walk on a graph and a balancing term …