Conformal Prediction in Hierarchical Classification With Constrained Representation Complexity

MCML Authors

Alireza Javanmardi

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Yusuf Sale

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Core PI

Artificial Intelligence and Machine Learning

Abstract

Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hierarchy, and propose two computationally efficient inference algorithms. The first algorithm returns internal nodes as prediction sets, while the second one relaxes this restriction. Using the notion of representation complexity, the latter yields smaller set sizes at the cost of a more general and combinatorial inference problem. Empirical evaluations on several benchmark datasets demonstrate the effectiveness of the proposed algorithms in achieving nominal coverage.

inproceedings MJS+26

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AISTATS 2026

29th International Conference on Artificial Intelligence and Statistics. Tangier, Morocco, May 02-05, 2026. To be published. Preprint available.

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Authors

T. Mortier • A. Javanmardi • Y. Sale • E. Hüllermeier • W. Waegeman

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Links

arXiv

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Research Area

 A3 | Computational Models