1 citations · 5 across the 22 of their papers we have counts for
9 papers · 1 filter
XU-RS: Explaining Credal Width in Random-Set Language Models
David Achara, Maryam Sultana, Alexander D. Rast +1
Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot tell whether that uncertainty…
Credal Large Language Models for Semantic Commitment under Uncertainty
Shireen Kudukkil Manchingal, Sofiia Nikolenko, Fabio Cuzzolin
Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central limitation is that standard LLMs represent uncertainty through a sing…
Random-Set Graph Neural Networks
Tommy Woodley, Shireen Kudukkil Manchingal, Matteo Tolloso +2
Uncertainty quantification has become an important factor in understanding the data representations produced by Graph Neural Networks (GNNs). Despite their predictive capabilities…
A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification
Ezel Kilicdere, Shireen Kudukkil Manchingal, Fabio Cuzzolin
Deep neural networks achieve high accuracy on image classification tasks. Yet, they often produce overconfident predictions as which fail to express epistemic uncertainty, and freq…
Statistical inference with belief functions: A survey
Fabio Cuzzolin
Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack of data renders learning a pro…
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-…
Epistemic Generative Adversarial Networks
Muhammad Mubashar, Fabio Cuzzolin
Generative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather than a wide range…
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…