ICML 2016poster15 citations
Pricing a Low-regret Seller
Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod
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.
BibTeX
@InProceedings{pmlr-v48-heidari16,
title = {Pricing a Low-regret Seller},
author = {Heidari, Hoda and Mahdian, Mohammad and Syed, Umar and Vassilvitskii, Sergei and Yazdanbod, Sadra},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {2559--2567},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/heidari16.pdf},
url = {https://proceedings.mlr.press/v48/heidari16.html},
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.}
}