Coreset-based Conformal Prediction for Large-scale Learning


Nery Riquelme-Granada, Khuong Nguyen, Zhiyuan Luo ;
Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 105:142-162, 2019.


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.

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