Conformal Prediction with Partially Labeled Data
Alireza Javanmardi Yusuf Sale Paul Hofman Eyke Hüllermeier
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.