1 citations · 5 across the 22 of their papers we have counts for
5 papers · 2 filters
Credal and Interval Deep Evidential Classifications
Michele Caprio, Shireen K. Manchingal, Fabio Cuzzolin
Uncertainty Quantification (UQ) presents a pivotal challenge in the field of Artificial Intelligence (AI), profoundly impacting decision-making, risk assessment and model reliabili…
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…
Epistemic Wrapping for Uncertainty Quantification
Maryam Sultana, Neil Yorke-Smith, Kaizheng Wang +3
Uncertainty estimation is pivotal in machine learning, especially for classification tasks, as it improves the robustness and reliability of models. We introduce a novel `Epistemic…
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…
A Unified Evaluation Framework for Epistemic Predictions
Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang +1
Predictions of uncertainty-aware models are diverse, ranging from single point estimates (often averaged over prediction samples) to predictive distributions, to set-valued or cred…