7 papers · 1 filter
Subjective Risk Decomposition: A New View for Uncertainty Quantification
Raghad Alamri, Michele Caprio, Gavin Brown
We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but instead consequences, of higher-level…
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
Julian Rodemann, Alexander Marquard, Thomas Augustin +1
Approximate Bayesian inference typically revolves around computing the posterior parameter distribution. In practice, however, the main object of interest is often a model's predic…
CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression
Luben M. C. Cabezas, Sabina J. Sloman, Bruno M. Resende +3
Conformal prediction delivers prediction intervals with distribution-free coverage, but its intervals can look overconfident in regions where the model is extrapolating, because st…
Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contamination
Mengqi Chen, Thomas B. Berrett, Theodoros Damoulas +1
Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set that can capture distributional shifts in out-of-sample environments. While…
Conformal Prediction Regions are Imprecise Highest Density Regions
Michele Caprio, Yusuf Sale, Eyke Hüllermeier
Recently, Cella and Martin proved how, under an assumption called consonance, a credal set (i.e. a closed and convex set of probabilities) can be derived from the conformal transdu…
Conformalized Credal Regions for Classification with Ambiguous Ground Truth
Michele Caprio, David Stutz, Shuo Li +1
An open question in \emph{Imprecise Probabilistic Machine Learning} is how to empirically derive a credal region (i.e., a closed and convex family of probabilities on the output sp…