185 citations · 293 across the 13 of their papers we have counts for
5 papers · 1 filter
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
Zhidi Lin, Juan Maroñas, Ying Li +2
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaus…
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
Richard Cornelius Suwandi, Zhidi Lin, Feng Yin +2
Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyper-parameter optimization. This…
Gaussian Processes for Analyzing Positioned Trajectories in Sports
Yuxin Zhao, Feng Yin, Fredrik Gunnarsson +1
Kernel-based machine learning approaches are gaining increasing interest for exploring and modeling large dataset in recent years. Gaussian process (GP) is one example of such kern…
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Feng Yin, Lishuo Pan, Xinwei He +4
Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However…
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
Yue Xu, Feng Yin, Wenjun Xu +2
The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic predict…