Gaussian processes are a powerful framework for quantifying uncertainty and for sequential
decision-making but are limited by the requirement of solving linear systems. In general, this …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …

This article deals with nonconvex stochastic optimization problems in deep learning.
Appropriate learning rates, based on theory, for adaptive-learning-rate optimization …

In machine learning, stochastic gradient descent (SGD) is widely deployed to train models
using highly non-convex objectives with equally complex noise models. Unfortunately, SGD …

Modern approaches to supervised learning like deep neural networks (DNNs) typically
implicitly assume that observed responses are statistically independent. In contrast …

Scaling hyperparameter optimisation to very large datasets remains an open problem in the
Gaussian process community. This paper focuses on iterative methods, which use linear …

Multi-output Gaussian process (MGP) regression plays an important role in the integrative
analysis of different but interrelated systems/units. Existing MGP approaches assume that …

High-cardinality categorical features are a major challenge for machine learning methods in
general and for deep learning in particular. Existing solutions such as one-hot encoding and …

Aim: Advanced sensing and imaging is capable to retrieve rich information of complex
systems, which can be integrated with underlying physics to develop a personalized …

Gaussian process (GP) regression is a flexible, nonparametric approach to regression that
naturally quantifies uncertainty. In many applications, the number of responses and …