  The problem of market clearing is to set a price for an item such that
quantity demanded equals quantity supplied. In this work, we cast the problem
of predicting clearing prices into a learning framework and use the resulting
models to perform revenue optimization in auctions and markets with contextual
information. The economic intuition behind market clearing allows us to obtain
fine-grained control over the aggressiveness of the resulting pricing policy,
grounded in theory. To evaluate our approach, we fit a model of clearing prices
over a massive dataset of bids in display ad auctions from a major ad exchange.
The learned prices outperform other modeling techniques in the literature in
terms of revenue and efficiency trade-offs. Because of the convex nature of the
clearing loss function, the convergence rate of our method is as fast as linear
regression.
