NIPS Proceedingsβ

Revenue Optimization with Approximate Bid Predictions

Part of: Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings


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Conference Event Type: Poster


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.