Pricing a Low-regret Seller

Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2559-2567, 2016.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Pricing a Low-regret Seller %A Hoda Heidari %A Mohammad Mahdian %A Umar Syed %A Sergei Vassilvitskii %A Sadra Yazdanbod %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-heidari16 %I PMLR %P 2559--2567 %U https://proceedings.mlr.press/v48/heidari16.html %V 48 %X 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.
RIS
TY - CPAPER TI - Pricing a Low-regret Seller AU - Hoda Heidari AU - Mohammad Mahdian AU - Umar Syed AU - Sergei Vassilvitskii AU - Sadra Yazdanbod BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-heidari16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 2559 EP - 2567 L1 - http://proceedings.mlr.press/v48/heidari16.pdf UR - https://proceedings.mlr.press/v48/heidari16.html AB - 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. ER -
APA
Heidari, H., Mahdian, M., Syed, U., Vassilvitskii, S. & Yazdanbod, S.. (2016). Pricing a Low-regret Seller. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:2559-2567 Available from https://proceedings.mlr.press/v48/heidari16.html.

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