Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

We introduce a new family of deep neural network models. Instead of specifying a discrete
sequence of hidden layers, we parameterize the derivative of the hidden state using a …

Accurately predicting the future capacity and remaining useful life of batteries is necessary to
ensure reliable system operation and to minimise maintenance costs. The complex nature of …

We introduce stochastic variational inference for Gaussian process models. This enables the
application of Gaussian process (GP) models to data sets containing millions of data points …

Bayesian optimization has recently been proposed as a framework for automatically tuning
the hyperparameters of machine learning models and has been shown to yield state-of-the …

Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …

In this paper, we propose an efficient algorithm, called vector field consensus, for
establishing robust point correspondences between two sets of points. Our algorithm starts …

In many practical applications of supervised learning the task involves the prediction of
multiple target variables from a common set of input variables. When the prediction targets …

Integrating solar energy with existing grid systems is difficult due to its variability, which is
impacted by factors such as the predicted horizon, meteorological conditions, and …