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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…
stat.ML2025
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…
stat.ML2024
Preventing Model Collapse in Gaussian Process Latent Variable Models
Ying Li, Zhidi Lin, Feng Yin +1
Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised learning models commonly used for dimensionality reduction. However, common challenges in mo…