Aggregating Algorithm for prediction of packs

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Title: Aggregating Algorithm for prediction of packs
Authors: Adamskiy, D
Bellotti, A
Dzhamtyrova, R
Kalnishkan, Y
Item Type: Journal Article
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.
Issue Date: 7-Jan-2019
Date of Acceptance: 25-Oct-2018
URI: http://hdl.handle.net/10044/1/65690
DOI: https://dx.doi.org/10.1007/s10994-018-5769-2
ISSN: 0885-6125
Publisher: Springer Nature
Journal / Book Title: Machine Learning
Copyright Statement: © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.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: 0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Artificial Intelligence & Image Processing
Publication Status: Published online
Online Publication Date: 2019-01-07
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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