Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

In collaborative filtering recommender systems user's preferences are expressed as ratings
for items, and each additional rating extends the knowledge of the system and affects 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 …

Deep learning methods have become the de-facto standard for challenging image
processing tasks such as image classification. One major hurdle of deep learning …

Even though active learning forms an important pillar of machine learning, deep learning
tools are not prevalent within it. Deep learning poses several difficulties when used in an …

Abstract Domain randomization is a popular technique for improving domain transfer, often
used in a zero-shot setting when the target domain is unknown or cannot easily be used for …

Function evaluations (FEs) of many real-world optimization problems are time or resource
consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to …

Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing
methods leverage advanced neural networks for prognostics performance improvement …

Surrogate-assisted, or meta-model based evolutionary computation uses efficient
computational models, often known as surrogates or meta-models, for approximating the …

The key idea behind active learning is that a machine learning algorithm can achieve
greater accuracy with fewer labeled training instances if it is allowed to choose the training …