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Aggregating Algorithm for Prediction of Packs

Adamskiy, D; Bellotti, T; Dzhamtyrova, R; Kalnishkan, Y; (2019) Aggregating Algorithm for Prediction of Packs. Machine Learning 10.1007/s10994-018-5769-2. (In press). Green open access

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Abstract

This paper formulates a protocol for prediction of packs, which is a special case of on-line prediction under delayed feedback. Under the prediction of packs protocol, the learner must make a few predictions without seeing the respective outcomes and then the outcomes are revealed in one go. The paper develops the theory of prediction with expert advice for packs by generalising the concept of mixability. We propose a number of merging algorithms for prediction of packs with tight worst case loss upper bounds similar to those for Vovk’s Aggregating Algorithm. Unlike existing algorithms for delayed feedback settings, our algorithms do not depend on the order of outcomes in a pack. Empirical experiments on sports and house price datasets are carried out to study the performance of the new algorithms and compare them against an existing method.

Type: Article
Title: Aggregating Algorithm for Prediction of Packs
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10994-018-5769-2
Publisher version: https://doi.org/10.1007/s10994-018-5769-2
Language: English
Additional information: © The Author(s) 2019. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creative commons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Machine learning, On-line learning, Prediction with expert advice, Sport, House prices
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10066713
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