3 accepted papers
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension, leading to escalating computational complexity and parameter proli…
This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating $C$ stochastic processes characterized by: 1) the $C$ processes are non-stationary, 2) the $C$ processes are dependent by construction without needing a mixing matrix, 3) training and making predictions is…
Gaussian Processes (GP) can be used as flexible, non-parametric function priors. Inspired by the growing body of work on Normalizing Flows, we enlarge this class of priors through a parametric invertible transformation that can be made input-dependent. Doing so also allows us to encode interpretable…