Real-Time Optimisation for Online Learning in Auctions

Lorenzo Croissant, Marc Abeille, Clement Calauzenes
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2217-2226, 2020.

Abstract

In display advertising, a small group of sellers and bidders face each other in up to $10^{12}$ auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online and produce a very high frequency stream of data. This results in a computational strain that requires algorithms be real-time. Unfortunately, existing methods inherited from the batch setting suffer $O(\sqrt{t})$ time/memory complexity at each update, prohibiting their use. In this paper, we provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-croissant20a, title = {Real-Time Optimisation for Online Learning in Auctions}, author = {Croissant, Lorenzo and Abeille, Marc and Calauzenes, Clement}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {2217--2226}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/croissant20a/croissant20a.pdf}, url = { http://proceedings.mlr.press/v119/croissant20a.html }, abstract = {In display advertising, a small group of sellers and bidders face each other in up to $10^{12}$ auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online and produce a very high frequency stream of data. This results in a computational strain that requires algorithms be real-time. Unfortunately, existing methods inherited from the batch setting suffer $O(\sqrt{t})$ time/memory complexity at each update, prohibiting their use. In this paper, we provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.} }
Endnote
%0 Conference Paper %T Real-Time Optimisation for Online Learning in Auctions %A Lorenzo Croissant %A Marc Abeille %A Clement Calauzenes %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-croissant20a %I PMLR %P 2217--2226 %U http://proceedings.mlr.press/v119/croissant20a.html %V 119 %X In display advertising, a small group of sellers and bidders face each other in up to $10^{12}$ auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online and produce a very high frequency stream of data. This results in a computational strain that requires algorithms be real-time. Unfortunately, existing methods inherited from the batch setting suffer $O(\sqrt{t})$ time/memory complexity at each update, prohibiting their use. In this paper, we provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.
APA
Croissant, L., Abeille, M. & Calauzenes, C.. (2020). Real-Time Optimisation for Online Learning in Auctions. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:2217-2226 Available from http://proceedings.mlr.press/v119/croissant20a.html .

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