1 citations · 5 across the 22 of their papers we have counts for
23 papers
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…
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…
Uncertainty-Aware Autonomous Vehicles: Predicting the Road Ahead
Shireen Kudukkil Manchingal, Armand Amaritei, Mihir Gohad +4
Autonomous Vehicle (AV) perception systems have advanced rapidly in recent years, providing vehicles with the ability to accurately interpret their environment. Perception systems…
Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
Shireen Kudukkil Manchingal, Andrew Bradley, Julian F. P. Kooij +3
Despite AI's impressive achievements, including recent advances in generative and large language models, there remains a significant gap in the ability of AI systems to handle unce…
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…
Random-Set Large Language Models
Muhammad Mubashar, Shireen Kudukkil Manchingal, Fabio Cuzzolin
Large Language Models (LLMs) are known to produce very high-quality tests and responses to our queries. But how much can we trust this generated text? In this paper, we study the p…
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…
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…
Feature boosting with efficient attention for scene parsing
Vivek Singh, Shailza Sharma, Fabio Cuzzolin
The complexity of scene parsing grows with the number of object and scene classes, which is higher in unrestricted open scenes. The biggest challenge is to model the spatial relati…
Generalising realisability in statistical learning theory under epistemic uncertainty
Fabio Cuzzolin
The purpose of this paper is to look into how central notions in statistical learning theory, such as realisability, generalise under the assumption that train and test distributio…
Credal Learning Theory
Michele Caprio, Maryam Sultana, Eleni Elia +1
Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from a…
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…
Reasoning with random sets: An agenda for the future
Fabio Cuzzolin
In this paper, we discuss a potential agenda for future work in the theory of random sets and belief functions, touching upon a number of focal issues: the development of a fully-f…