Pricing a low-regret seller
Venue
Proceedings of the Thirty-Third International Conference on Machine Learning (ICML 2016)
Publication Year
2016
Authors
Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod
BibTeX
Abstract
As the number of ad exchanges has grown, publishers have turned to low regret
learning algorithms to decide which exchange offers the best price for their
inventory. This in turn opens the following question for the exchange: how to set
prices to attract as many sellers as possible and maximize revenue. In this work we
formulate this precisely as a learning problem, and present algorithms showing that
by simply knowing that the counterparty is using a low regret algorithm is enough
for the exchange to have its own low regret learning algorithm to find the optimal
price.
