3 papers
stat.ML2025
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
Zhidi Lin, Ying Li, Feng Yin +2
Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existi…
cs.LG2025
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…
eess.SP2024
Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction
Yiyong Sun, Jiajun He, Zhidi Lin +3
Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel predic…