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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2022.82
URN: urn:nbn:de:0030-drops-156785
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/15678/
Gupta, Varun ;
Jung, Christopher ;
Noarov, Georgy ;
Pai, Mallesh M. ;
Roth, Aaron
Online Multivalid Learning: Means, Moments, and Prediction Intervals
Abstract
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples (x,y). This means that the resulting estimates correctly predict various statistics of the labels y not just marginally - as averaged over the sequence of examples - but also conditionally on x ∈ G for any G belonging to an arbitrary intersecting collection of groups ?.
We provide three instantiations of this framework. The first is mean prediction, which corresponds to an online algorithm satisfying the notion of multicalibration from [Hébert-Johnson et al., 2018]. The second is variance and higher moment prediction, which corresponds to an online algorithm satisfying the notion of mean-conditioned moment multicalibration from [Jung et al., 2021]. Finally, we define a new notion of prediction interval multivalidity, and give an algorithm for finding prediction intervals which satisfy it. Because our algorithms handle adversarially chosen examples, they can equally well be used to predict statistics of the residuals of arbitrary point prediction methods, giving rise to very general techniques for quantifying the uncertainty of predictions of black box algorithms, even in an online adversarial setting. When instantiated for prediction intervals, this solves a similar problem as conformal prediction, but in an adversarial environment and with multivalidity guarantees stronger than simple marginal coverage guarantees.
BibTeX - Entry
@InProceedings{gupta_et_al:LIPIcs.ITCS.2022.82,
author = {Gupta, Varun and Jung, Christopher and Noarov, Georgy and Pai, Mallesh M. and Roth, Aaron},
title = {{Online Multivalid Learning: Means, Moments, and Prediction Intervals}},
booktitle = {13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
pages = {82:1--82:24},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-217-4},
ISSN = {1868-8969},
year = {2022},
volume = {215},
editor = {Braverman, Mark},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/15678},
URN = {urn:nbn:de:0030-drops-156785},
doi = {10.4230/LIPIcs.ITCS.2022.82},
annote = {Keywords: Uncertainty Estimation, Calibration, Online Learning}
}
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Keywords: |
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Uncertainty Estimation, Calibration, Online Learning |
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Collection: |
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13th Innovations in Theoretical Computer Science Conference (ITCS 2022) |
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Issue Date: |
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2022 |
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Date of publication: |
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25.01.2022 |
