Tuesday, January 24, 2017. 12:00PM. NSH 3305.

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Hoda Heidari - Pricing a Low-regret Seller

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 in order to maximize revenue. In this work, we formulate this precisely as a learning problem, and present algorithms showing that simply knowing the counterparty is using a low regret algorithm is enough for the exchange to have its own low regret learning algorithm for the optimal price.

(Joint work with Mohammad Mahdian, Umar Syed, Sergei Vassilvistkii, and Sadra Yazdanbod. Appeared in ICML'16)