Correcting Model Bias with Sparse Implicit Processes. (arXiv:2207.10673v1 [stat.ML])

Model selection in machine learning (ML) is a crucial part of the Bayesian
learning procedure. Model choice may impose strong biases on the resulting
predictions, which can hinder the performance of methods such as Bayesian
neural networks and neural samplers. On the other hand, newly proposed
approaches for Bayesian ML exploit features of approximate inference in
function space with implicit stochastic processes (a generalization of Gaussian
processes). The approach of Sparse Implicit Processes (SIP) is particularly
successful in this regard, …

arxiv bias ml model bias processes