Learning to Clear the Market

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 apply the resulting models to the problem of 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 techniques in the literature in terms of revenue and efficiency trade-offs. Because of the convex nature of the clearing loss function, our method has very fast convergence, as fast as linear regression over the same dataset.