4 papers
Simulation-Free Learning of GP-SDEs from Irregular Observations
Zhidi Lin, Yuhao Liu, Ying Li +2
Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learn…
Multi-View Oriented GPLVM: Expressiveness and Efficiency
Zi Yang, Ying Li, Zhidi Lin +2
The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel ex…
Scalable Random Feature Latent Variable Models
Ying Li, Zhidi Lin, Yuhao Liu +3
Random feature latent variable models (RFLVMs) represent the state-of-the-art in latent variable models, capable of handling non-Gaussian likelihoods and effectively uncovering pat…
Online/Offline Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models
Ying Li, Zhidi Lin, Kai Li +1
Robust beamforming is a pivotal technique in massive multiple-input multiple-output (MIMO) systems as it mitigates interference among user equipment (UE). One current risk-neutral…