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Conformal Prediction With Partially Labeled Data

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Link to Profile Eyke Hüllermeier PI Matchmaking

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

COPA 2023

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

Authors

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

Links

URL

Research Area

 A3 | Computational Models

BibTeXKey: JSH+23

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