Aggregating algorithm for prediction of packs
File(s)
Published version
Author(s)
Adamskiy, Dmitry
Bellotti, Anthony
Dzhamtyrova, Raisa
Kalnishkan, Yuri
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.
Date Issued
2019-09-01
Date Acceptance
2018-10-25
Citation
Machine Learning, 2019, 108 (8-9), pp.1231-1260
ISSN
0885-6125
Publisher
Springer Nature
Start Page
1231
End Page
1260
Journal / Book Title
Machine Learning
Volume
108
Issue
8-9
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.
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Machine learning
On-line learning
Prediction with expert advice
Sport
House prices
0801 Artificial Intelligence and Image Processing
1702 Cognitive Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Date Publish Online
2019-01-07