ORCID: 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.
Item Type: | Conference or Workshop Item (Paper) |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
URN: | urn:nbn:de:bvb:19-epub-107487-4 |
Item ID: | 107487 |
Date Deposited: | 23. Oct 2023 10:20 |
Last Modified: | 11. Oct 2024 13:06 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 451737409 |