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cs.LG2026
AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions
Jamie Duell, Alejandro Jimenez Rodriguez, Mahault Albarracin
Counterfactual explanations formalize "what-if" scenarios by identifying modifications to an input instance that obtain a desired alternative prediction. Traditionally, whether gen…
cs.LG2026
Explainable Uncertainty Estimation for Reliable Medical AI
Li Rong Wang, Jamie Duell, Xinran Xu +6
Artificial intelligence has strong potential to support clinical decision-making, yet its adoption in healthcare remains limited due to a lack of trust. Uncertainty estimation can…
cs.LG2026
Provably Robust Bayesian Counterfactual Explanations under Model Changes
Jamie Duell, Xiuyi Fan
Counterfactual explanations (CEs) offer interpretable insights into machine learning predictions by answering ``what if?" questions. However, in real-world settings where models ar…