1 citations · 5 across the 22 of their papers we have counts for
8 papers · 1 filter
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…