Conformalized Credal Set Predictors

Javanmardi, Alireza; Stutz, David und Hüllermeier, Eyke (December 2024): Conformalized Credal Set Predictors. The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, BC, Canada, 10. -15. December 2024.

Abstract

Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution. In machine learning, they have recently attracted attention as an appealing formalism for uncertainty representation, in particular, due to their ability to represent both the aleatoric and epistemic uncertainty in a prediction. However, the design of methods for learning credal set predictors remains a challenging problem. In this paper, we make use of conformal prediction for this purpose. More specifically, we propose a method for predicting credal sets in the classification task, given training data labeled by probability distributions. Since our method inherits the coverage guarantees of conformal prediction, our conformal credal sets are guaranteed to be valid with high probability (without any assumptions on model or distribution). We demonstrate the applicability of our method on ambiguous classification tasks for uncertainty quantification.

• Item Type: Conference or Workshop Item (Paper)
• 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
• Language: English
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 451737409