39 citations · 56 across the 7 of their papers we have counts for
8 papers
Fortuna: A Library for Uncertainty Quantification in Deep Learning
Gianluca Detommaso, Alberto Gasparin, Michele Donini +3
We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that ca…
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…
Causal Bias Quantification for Continuous Treatments
Gianluca Detommaso, Michael Brückner, Philip Schulz +1
We extend the definition of the marginal causal effect to the continuous treatment setting and develop a novel characterization of causal bias in the framework of structural causal…
Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems
Tiangang Cui, Gianluca Detommaso, Robert Scheichl
We present a non-trivial integration of dimension-independent likelihood-informed (DILI) MCMC (Cui, Law, Marzouk, 2016) and the multilevel MCMC (Dodwell et al., 2015) to explore th…
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…
Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies
Gianluca Detommaso, Tim Dodwell, Rob Scheichl
In this paper, we present a generalisation of the Multilevel Monte Carlo (MLMC) method to a setting where the level parameter is a continuous variable. This Continuous Level Monte…