Real-Time Optimisation for Online Learning in Auctions

Autor:	Croissant, Lorenzo, Abeille, Marc, Calauzènes, Clément
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Computer Science and Game Theory Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: International Conference on Machine Learning 2020, Jul 2020, Vienna, Austria
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.10070 Zobrazit plný text záznamu View this record from Arxiv