2 citations · 2 across the 4 of their papers we have counts for
5 papers
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
Kai Chen, Qinglei Kong, Yijue Dai +4
Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning, next-generation data-driven communication sys…
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting
Kai Chen, Twan van Laarhoven, Elena Marchiori
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 d…
Multi-Output Convolution Spectral Mixture for Gaussian Processes
Kai Chen, Twan van Laarhoven, Perry Groot +2
Multi-output Gaussian processes (MOGPs) are an extension of Gaussian Processes (GPs) for predicting multiple output variables (also called channels, tasks) simultaneously. In this…
Multitask Gaussian Process with Hierarchical Latent Interactions
Kai Chen, Twan van Laarhoven, Elena Marchiori +2
Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent lat…
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes
Kai Chen, Yijue Dai, Feng Yin +2
Spectral mixture (SM) kernels comprise a powerful class of generalized kernels for Gaussian processes (GPs) to describe complex patterns. This paper introduces model compression an…