We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence.

Inthisworkwepresenta scalable quasi-Bayesian procedure for IV regression, building upon the recently developed kernelized IV models.

Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained network, and they scale to large models and datasets.