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# Coreset-based Conformal Prediction for Large-scale Learning

; Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 105:142-162, 2019.

#### Abstract

As the volume of data increase rapidly,
most traditional machine learning algorithms become computationally prohibitive.
Furthermore, the available data can be so big that a single machine’s memory can easily be overflown.
We propose Coreset-Based Conformal Prediction,
a strategy for dealing with big data by applying conformal predictors
to a weighted summary of data—namely the coreset.
We compare our approach against standalone inductive conformal predictors
over three large competition-grade datasets
to demonstrate that our coreset-based strategy may not only significantly improve the learning speed,
but also retains predictions validity and the predictors’ efficiency.