mlsanityData to 7 Oct 2026

Revenue Optimization with Approximate Bid Predictions

arXiv 1706.04732 ↗First posted 15 Jun 2017cs.LG cs.GTcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2017.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 1; 2024: 4; 2025: 0; 2026: 1.

21 papers.

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  1. 2024Improving Online Algorithms via ML Predictionscited by 65
  2. 2022Online Algorithms with Multiple Predictionscited by 22
  3. 2022Customizing ML Predictions for Online Algorithmscited by 21
  4. 2022Algorithms with Prediction Portfolioscited by 19
  5. 2021Can Q-Learning be Improved with Advice?cited by 13
  6. 2022A Regression Approach to Learning-Augmented Online Algorithmscited by 12
  7. 2021Double Coverage with Machine-Learned Advicecited by 10
  8. 2021Robustification of Online Graph Exploration Methodscited by 10
  9. 2021Adversarial Deep Learning for Online Resource Allocationcited by 7
  10. 2023Online Ad Allocation with Predictionscited by 7
  11. 2024Non-clairvoyant Scheduling with Partial Predictionscited by 7
  12. 2021Loss Functions for Discrete Contextual Pricing with Observational Datacited by 6
9 more
  1. 2021Improved Bounds for Online Facility Location with Predictionscited by 5
  2. 2022Contextual Pandora's Boxcited by 5
  3. 2022Regulatory Instruments for Fair Personalized Pricingcited by 4
  4. 2021Sequential Ski Rental Problemcited by 3
  5. 2024Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Modelcited by 3
  6. 2022Convex Surrogate Loss Functions for Contextual Pricing with Transaction Datacited by 1
  7. 2022Online TSP with Predictionscited by 1
  8. 2024Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictionscited by 1
  9. 2026Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implicationscited by 0