Reviews: Revenue Optimization with Approximate Bid Predictions

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.