In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types, and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor. This is the first result that formally relates the revenue gained to the quality of a standard machine learned model.

It is motivated by ad auction design where there are trillions of different items being sold. For many items, there is often little or no information available about the bidder's value for that precise good. As a result, previous techniques in sample-based auction design do not apply because the auction designer has no samples from the bidder's value distribution for many of the items. Meanwhile, ads (for example) are often easily parameterized by feature vectors, so the authors make the assumption that items with similar features have similar bid distributions. Under this assumption, the authors show how to set prices and bound the revenue loss.

In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types, and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor. This is the first result that formally relates the revenue gained to the quality of a standard machine learned model.