1 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
Sufficient Decision Proxies for Decision-Focused Learning
Noah Schutte, Grigorii Veviurko, Krzysztof Postek +1
When solving optimization problems under uncertainty with contextual data, utilizing machine learning to predict the uncertain parameters' values is a popular and effective approac…
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…
Robust Losses for Decision-Focused Learning
Noah Schutte, Krzysztof Postek, Neil Yorke-Smith
Optimization models used to make discrete decisions often contain uncertain parameters that are context-dependent and estimated through prediction. To account for the quality of th…