1 citations · 2 across the 7 of their papers we have counts for
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Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model
Ghifari Adam Faza, Jolan Wauters, Fabio Cuzzolin +2
In engineering, uncertainty propagation aims to characterise system outputs under uncertain inputs. For interval uncertainty, the goal is to determine output bounds given interval-…
Learning Credal Ensembles via Distributionally Robust Optimization
Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin +3
Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epist…
Credal Ensemble Distillation for Uncertainty Quantification
Kaizheng Wang, Fabio Cuzzolin, David Moens +1
Deep ensembles (DE) have emerged as a powerful approach for quantifying predictive uncertainty and distinguishing its aleatoric and epistemic components, thereby enhancing model ro…
Generalized Decision Focused Learning under Imprecise Uncertainty--Theoretical Study
Keivan Shariatmadar, Neil Yorke-Smith, Ahmad Osman +3
Decision Focused Learning has emerged as a critical paradigm for integrating machine learning with downstream optimisation. Despite its promise, existing methodologies predominantl…
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar +2
This paper presents an innovative approach, called credal wrapper, to formulating a credal set representation of model averaging for Bayesian neural networks (BNNs) and deep ensemb…
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
Kaizheng Wang, Keivan Shariatmadar, Shireen Kudukkil Manchingal +3
Effective uncertainty estimation is becoming increasingly attractive for enhancing the reliability of neural networks. This work presents a novel approach, termed Credal-Set Interv…