Online Learning Using Only Peer Prediction

Yang Liu, Dave Helmbold
; Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2032-2042, 2020.

Abstract

This paper considers a variant of the classical online learning problem with expert predictions. Our model’s differences and challenges are due to lacking any direct feedback on the loss each expert incurs at each time step $t$. We propose an approach that uses peer prediction and identify conditions where it succeeds. Our techniques revolve around a carefully designed peer score function $s()$ that scores experts’ predictions based on the peer consensus. We show a sufficient condition, that we call \emph{peer calibration}, under which standard online learning algorithms using loss feedback computed by the carefully crafted $s()$ have bounded regret with respect to the unrevealed ground truth values. We then demonstrate how suitable $s()$ functions can be derived for different assumptions and models.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-liu20d, title = {Online Learning Using Only Peer Prediction}, author = {Liu, Yang and Helmbold, Dave}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {2032--2042}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, address = {Online}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/liu20d/liu20d.pdf}, url = {http://proceedings.mlr.press/v108/liu20d.html}, abstract = {This paper considers a variant of the classical online learning problem with expert predictions. Our model’s differences and challenges are due to lacking any direct feedback on the loss each expert incurs at each time step $t$. We propose an approach that uses peer prediction and identify conditions where it succeeds. Our techniques revolve around a carefully designed peer score function $s()$ that scores experts’ predictions based on the peer consensus. We show a sufficient condition, that we call \emph{peer calibration}, under which standard online learning algorithms using loss feedback computed by the carefully crafted $s()$ have bounded regret with respect to the unrevealed ground truth values. We then demonstrate how suitable $s()$ functions can be derived for different assumptions and models.} }
Endnote
%0 Conference Paper %T Online Learning Using Only Peer Prediction %A Yang Liu %A Dave Helmbold %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-liu20d %I PMLR %J Proceedings of Machine Learning Research %P 2032--2042 %U http://proceedings.mlr.press %V 108 %W PMLR %X This paper considers a variant of the classical online learning problem with expert predictions. Our model’s differences and challenges are due to lacking any direct feedback on the loss each expert incurs at each time step $t$. We propose an approach that uses peer prediction and identify conditions where it succeeds. Our techniques revolve around a carefully designed peer score function $s()$ that scores experts’ predictions based on the peer consensus. We show a sufficient condition, that we call \emph{peer calibration}, under which standard online learning algorithms using loss feedback computed by the carefully crafted $s()$ have bounded regret with respect to the unrevealed ground truth values. We then demonstrate how suitable $s()$ functions can be derived for different assumptions and models.
APA
Liu, Y. & Helmbold, D.. (2020). Online Learning Using Only Peer Prediction. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in PMLR 108:2032-2042

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