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: Building the next-generation wireless systems that could support services such as the metaverse, digital twins (DTs), and holographic teleportation is challenging to achieve exclusively ...

Abstract: Data-driven methods, especially reinforcement learning (RL), are adept at addressing uncertainties but are poor at ensuring safety, which is a critical requirement in active distribution ...

Abstract: Multiview subspace clustering (MSC) maximizes the utilization of complementary description information provided by multiview data and achieves impressive clustering performance. However, ...

Abstract: Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the ...

Abstract: Deep learning offers efficient solutions for drug-target interaction prediction, but current methods often fail to capture the full complexity of multi-modal data (i.e. sequence, graphs, and ...

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: This article highlights the critical role of reliable dc-dc converter operation in ensuring the stability of power conversion systems, especially in extreme environments like underwater ...

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: This paper explores the integration of Artificial Intelligence into 6G networks, focusing on optimizing communication, resource allocation, and enhancing security. As communication systems ...