Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past ...

Abstract: Online learning is a well established learning paradigm which has both theoretical and practical appeals. The goal of online learning is to make a sequence of accurate predictions given ...

Abstract: Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications ...

Abstract: This paper proposes a novel distributionally robust energy and reserve dispatch model with distributed renewable predictions. Through leveraging the prediction information from both the ...

Abstract: Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond. The prowess of LLMs is ...

Abstract: Motivated by diverse secure requirements of multiuser in unmanned aerial vehicle (UAV) systems, we propose a collaborative secret and covert transmission method for multi-antenna ground ...

Abstract: This paper presents a hybrid Energy Storage System (ESS) for DC microgrids, highlighting its potential for supporting future grid functions with high Renewable Energy Sources (RESs) ...