Coreset-based Conformal Prediction for large-scale learning

Nery Riquelme-Granada, Khuong An Nguyen, Zhiyuan Luo

Research output: Contribution to conferencePaper

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 stand-alone 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.
Original languageEnglish
Pages142-162
Publication statusPublished - 2019
Event8th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2019) - , Bulgaria
Duration: 9 Sep 201911 Sep 2019
https://cml.rhul.ac.uk/copa2019/

Conference

Conference8th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2019)
CountryBulgaria
Period9/09/1911/09/19
Internet address

Keywords

  • logistic regression
  • conformal predictors
  • importance sampling

Fingerprint Dive into the research topics of 'Coreset-based Conformal Prediction for large-scale learning'. Together they form a unique fingerprint.

  • Cite this

    Riquelme-Granada, N., Nguyen, K. A., & Luo, Z. (2019). Coreset-based Conformal Prediction for large-scale learning. 142-162. Paper presented at 8th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2019), Bulgaria. http://khuong.uk/Papers/coresetbased_conformal_prediction.pdf