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Poster
Revenue Optimization with Approximate Bid Predictions
Andres Munoz Medina · Sergei Vassilvitskii

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #223
in Posters Wed »

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.

Keywords: Clustering · Learning Theory · Regression · Non-Convex Optimization · Game Theory and Computational Economics
[ Paper] 

Author Information

Andres Munoz Medina (Google)
Sergei Vassilvitskii (Google)

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