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stat.ML2026
Continuous-depth Deep Gaussian Processes
Ying Li, Sanyou Wu, Zi Yang +6
Deep Gaussian processes (DGPs) are appealing Bayesian regression models, but their standard discrete-depth parameterization often becomes harder to optimize as depth grows. We argu…
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…