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Auction theory traditionally assumes that bidders' valuation distributionsare known to the auctioneer, such as in the celebrated, revenue-optimal Myersonauction. However, this theory does not describe how the auctioneer comes topossess this information. Recently, Cole and Roughgarden [2014] showed that anapproximation based on a finite sample of independent draws from each bidder'sdistribution is sufficient to produce a near-optimal auction. In this work, weconsider the problem of learning bidders' valuation distributions from muchweaker forms of observations. Specifically, we consider a setting where thereis a repeated, sealed-bid auction with $n$ bidders, but all we observe for eachround is who won, but not how much they bid or paid. We can also participate(i.e., submit a bid) ourselves, and observe when we win. From this information,our goal is to (approximately) recover the inherently recoverable part of theunderlying bid distributions. We also consider extensions where differentsubsets of bidders participate in each round, and where bidders' valuationshave a common-value component added to their independent private values.

Learning Valuation Distributions from Partial Observation