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Title: Real-time optimisation for online learning in auctions Authors:  Lorenzo Croissant - Universite Paris Dauphine - PSL (France) [presenting]
Marc Abeille - Criteo AI Lab (France)
Clement Calauzenes - Criteo AI Lab (France)
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 to be real-time. Unfortunately, existing methods inherited from the batch setting suffer $O(\sqrt{t})$ time/memory complexity at each update, prohibiting their use. We provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.