Conformal Prediction With Partially Labeled Data

MCML Authors

Alireza Javanmardi

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Yusuf Sale

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Paul Hofman

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

Abstract

While the predictions produced by conformal prediction are set-valued, the data used for training and calibration is supposed to be precise. In the setting of superset learning or learning from partial labels, a variant of weakly supervised learning, it is exactly the other way around: training data is possibly imprecise (set-valued), but the model induced from this data yields precise predictions. In this paper, we combine the two settings by making conformal prediction amenable to set-valued training data. We propose a generalization of the conformal prediction procedure that can be applied to set-valued training and calibration data. We prove the validity of the proposed method and present experimental studies in which it compares favorably to natural baselines.

inproceedings JSH+23

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COPA 2023

12th Symposium on Conformal and Probabilistic Prediction with Applications. Limassol, Cyprus, Sep 13-15, 2023.

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Authors

A. Javanmardi • Y. Sale • P. Hofman • E. Hüllermeier

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Links

URL

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Research Area

 A3 | Computational Models