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

Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo

Research output: Contribution to journalConference articlepeer-review


We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in advertising auctions. Existing works either only focus 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 various additional assumptions on the parametric form of the distribution to derive loss functions for predicting CTRs. In this work, we bring back the welfare objectives of ad auctions into CTR predictions and propose a novel weighted rankloss to train the CTR model. Compared to existing literature, our approach provides a provable guarantee on welfare but without assumptions on the eCPMs' distribution while also avoiding the intractability of naively applying existing learning-to-rank methods. Further, we propose a theoretically justifiable technique for calibrating the losses using labels generated from a teacher network, only assuming that the teacher network has bounded ℓ2 generalization error. Finally, we demonstrate the advantages of the proposed loss on synthetic and real-world data.

Original languageEnglish (US)
Pages (from-to)23239-23263
Number of pages25
JournalProceedings of Machine Learning Research
StatePublished - 2023
Externally publishedYes
Event40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States
Duration: Jul 23 2023Jul 29 2023

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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