14 citations · 16 across the 11 of their papers we have counts for
4 papers · 1 filter
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference
Zhidi Lin, Yiyong Sun, Feng Yin +1
The Gaussian process state-space models (GPSSMs) represent a versatile class of data-driven nonlinear dynamical system models. However, the presence of numerous latent variables in…
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…
Towards Flexibility and Interpretability of Gaussian Process State-Space Model
Zhid Lin, Feng Yin, Juan Maroñas
The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade. However, the standard GP with a preliminary kernel, such as the squared exp…