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We study the design of loss functions for click-through rates (CTR) tooptimize (social) welfare in advertising auctions. Existing works either onlyfocus on CTR predictions without consideration of business objectives (e.g.,welfare) in auctions or assume that the distribution over the participants'expected cost-per-impression (eCPM) is known a priori, then use variousadditional assumptions on the parametric form of the distribution to deriveloss functions for predicting CTRs. In this work, we bring back the welfareobjectives of ad auctions into CTR predictions and propose a novel weightedrankloss to train the CTR model. Compared to existing literature, our approachprovides a provable guarantee on welfare but without assumptions on the eCPMs'distribution while also avoiding the intractability of naively applyingexisting learning-to-rank methods. Further, we propose a theoreticallyjustifiable technique for calibrating the losses using labels generated from ateacher network, only assuming that the teacher network has bounded $\ell_2$generalization error. Finally, we demonstrate the advantages of the proposedloss on synthetic and real-world data.

Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions