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cs.LG2023
Sparse Variational Student-t Processes
Jian Xu, Delu Zeng
The theory of Bayesian learning incorporates the use of Student-t Processes to model heavy-tailed distributions and datasets with outliers. However, despite Student-t Processes hav…
cs.LG2023
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
Jian Xu, Shian Du, Junmei Yang +2
Deep Gaussian Process (DGP) models offer a powerful nonparametric approach for Bayesian inference, but exact inference is typically intractable, motivating the use of various appro…
cs.LG2023
Bayesian Gaussian Process ODEs via Double Normalizing Flows
Jian Xu, Shian Du, Junmei Yang +3
Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equatio…