Pricing a Low-regret Seller

  title={Pricing a Low-regret Seller},
  author={Hoda Heidari and Mohammad Mahdian and Umar Syed and Sergei Vassilvitskii and Sadra Yazdanbod},
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… CONTINUE READING

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