ORC IAP Seminar 2019 - Negin Golrezaei скачать в хорошем качестве

ORC IAP Seminar 2019 - Negin Golrezaei

ORC IAP Seminar 2019: Machine Learning and Operations Research Negin Golrezaei "Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions" http://orc.mit.edu/events/orc-iap-sem... Abstract Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buyers’ valuations for an item depend on the context that describes the item. However, the seller is not aware of the relationship between the context and buyers’ valuations, i.e., buyers’ preferences. The seller’s goal is to design a learning policy to set reserve prices via observing the past sales data, and her objective is to minimize her regret for revenue, where the regret is computed against a clairvoyant policy that knows buyers’ heterogeneous preferences. Given the seller’s goal, utility-maximizing buyers have the incentive to bid untruthfully in order to manipulate the seller’s learning policy. We propose two learning policies that are robust to such strategic behavior. These policies use the outcomes of the auctions, rather than the submitted bids, to estimate the preferences while controlling the long-term effect of the outcome of each auction on the future reserve prices. The first policy called Contextual Robust Pricing (CORP) is designed for the setting where the market noise distribution is known to the seller and achieves a T-period regret of O(dlog(Td)log(T)), where d is the dimension of the contextual information. The second policy, which is a variant of the first policy, is called Stable CORP (SCORP). This policy is tailored to the setting where the market noise distribution is unknown to the seller and belongs to an ambiguity set. We show that the SCORP policy has a T-period regret of O( dlog(Td)T^(2/3)). Bio Negin Golrezaei is an Assistant Professor of Operations Management at the MIT Sloan School of Management. Her current research interests are in the area of machine learning, statistical learning theory, mechanism design, and optimization algorithms with applications to revenue management, pricing, and online markets. Before joining MIT, Negin spent a year as a postdoctoral fellow at Google Research in New York where she worked with the Market Algorithm team to develop, design, and test new mechanisms and algorithms for online marketplaces. She is the recipient of the 2017 George B. Dantzig Dissertation Award; the INFORMS Revenue Management and Pricing Section Dissertation Prize; University of Southern California (USC) Ph.D. Achievement Award (2017), and USC CAMS Graduate Student Prize, for excellence in research with a substantial mathematical component (2017), and USC Provost's Ph.D. Fellowship (2011). Negin received her BSc (2007) and MSc (2009) degrees in electrical engineering from the Sharif University of Technology, Iran, and a Ph.D. (2017) in operations research from USC.