Data-driven paradigms are well-known and salient demands of future wireless
communication. Empowered by big data and machine learning techniques, next-generation …

We describe a framework for constructing nonstationary nonseparable random fields based
on an infinite mixture of convolved stochastic processes. When the mixing process is …

Data-driven paradigms are well-known and salient demands of future wireless
communication. Empowered by big data and machine learning, next-generation data-driven …

The modeling capabilities of a Gaussian Process (GP), such as generalization, nonlinearity,
and smoothness, are largely determined by the choice of its kernel. A popular family of …

Multioutput Gaussian processes (MOGPs) are an extension of Gaussian processes (GPs) for
predicting multiple output variables (also called channels/tasks) simultaneously. In this …

Multi-objective Bayesian optimization is a sequential optimization strategy in which an
optimizer searches for optimal solutions by maximizing an acquisition function. Most existing …

Long-term forecasting involves predicting a horizon that is far ahead of the last observation.
It is a problem of high practical relevance, for instance for companies in order to decide upon …

The development of material informatics has led to an increasingly deep intersection
between material science and machine learning (ML). However, limited data volume …

Multitask Gaussian processes (MTGPs) are a powerful approach for modeling
dependencies between multiple related tasks or functions for joint regression. Current …

Numerous techniques have been meticulously designed to achieve optimal architectures for
convolutional neural networks (CNNs), yet a comparable focus on vision transformers (ViTs) …