39 citations · 56 across the 7 of their papers we have counts for
4 papers · 1 filter
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors
Gianluca Detommaso, Alberto Gasparin, Andrew Wilson +1
As we move away from the data, the predictive uncertainty should increase, since a great variety of explanations are consistent with the little available information. We introduce…
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Jakob Kruse, Gianluca Detommaso, Ullrich Köthe +1
Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually af…
Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks
Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui +1
Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems. In this work, we introduce a Stei…
A Stein variational Newton method
Gianluca Detommaso, Tiangang Cui, Alessio Spantini +2
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullbac…