Credal Prediction based on Relative Likelihood

Löhr, Timo ; Hofman, Paul; Mohr, Felix und Hüllermeier, Eyke (December 2025): Credal Prediction based on Relative Likelihood. 39th Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA, USA, 2.- 7. December 2025. Advances in Neural Information Processing Systems. Vol. 38 San Diego, CA, USA:

Abstract

Predictions in the form of sets of probability distributions, so-called credal sets, provide a suitable means to represent a learner’s epistemic uncertainty. In this paper, we propose a theoretically grounded approach to credal prediction based on the statistical notion of relative likelihood: The target of prediction is the set of all (conditional) probability distributions produced by the collection of plausible models, namely those models whose relative likelihood exceeds a specified threshold. This threshold has an intuitive interpretation and allows for controlling the trade-off between correctness and precision of credal predictions. We tackle the problem of approximating credal sets defined in this way by means of suitably modified ensemble learning techniques. To validate our approach, we illustrate its effectiveness by experiments on benchmark datasets demonstrating superior uncertainty representation without compromising predictive performance. We also compare our method against several state-of-the-art baselines in credal prediction.

• Item Type: Conference or Workshop Item (Paper)
• EU Funded Grant Agreement Number: 101073307
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Place of Publication: San Diego, CA, USA