Logo Logo
Help
Contact
Switch Language to German

Javanmardi, Alireza; Sale, Yusuf; Hofman, Paul und 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 und Carlsson, Lars (eds.) : Vol. 204 PMLR. pp. 251-266 [PDF, 343kB]

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.

Actions (login required)

View Item View Item