8 papers
Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study
Kaizheng Wang, Yunjia Wang, Fabio Cuzzolin +3
Epistemic uncertainty in neural networks is commonly modeled using two second-order paradigms: distribution-based representations, which rely on posterior parameter distributions,…
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…
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…
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…
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…