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Javanmardi, Alireza; Sale, Yusuf; Hofman, Paul and Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 (September 2023): Conformal Prediction with Partially Labeled Data. Twelfth Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023), Limassol, Cyprus, September 13-15, 2023. Papadopoulos, Harris; Nguyen, Khuong An; Boström, Henrik and Carlsson, Lars (eds.) : Vol. 204 PMLR. pp. 251-266

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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.

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