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Multi-Platform Budget Management in Ad Markets with Non-IC Auctions (2306.07352v1)

Published 12 Jun 2023 in cs.GT, cs.LG, math.OC, and stat.ML

Abstract: In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation. Additionally, we investigate the online setting where the advertiser must submit bids across platforms while learning about other bidders' bids over time. Our algorithm has $O(T{3/4})$ regret under the full-information setting. Finally, we demonstrate that our algorithms have superior cumulative regret on both synthetic and real-world datasets of ad placement auctions, compared to existing adaptive pacing algorithms.

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References (40)
  1. Ad delivery with budgeted advertisers: A comprehensive LP approach. Journal of Electronic Commerce Research 9 (01 2008).
  2. Autobidding with constraints. In International Conference on Web and Internet Economics. Springer, 17–30.
  3. Stochastic Bandits for Multi-Platform Budget Optimization in Online Advertising. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21). Association for Computing Machinery, New York, NY, USA, 2805–2817. https://doi.org/10.1145/3442381.3450074
  4. On Revenue Maximization in Second-Price Ad Auctions. In Algorithms - ESA 2009 (Lecture Notes in Computer Science, Vol. 5757), Amos Fiat and Peter Sanders (Eds.). Springer, Berlin, Heidelberg, 155–166.
  5. Budget management strategies in repeated auctions. In Proceedings of the 26th International Conference on World Wide Web. 15–23.
  6. Santiago R Balseiro and Yonatan Gur. 2019. Learning in repeated auctions with budgets: Regret minimization and equilibrium. Management Science 65, 9 (2019), 3952–3968.
  7. Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees. In Proceedings of the 23rd ACM Conference on Economics and Computation. 476–476.
  8. Dimitri P Bertsekas. 1997. Nonlinear programming. Journal of the Operational Research Society 48, 3 (1997), 334–334.
  9. Price manipulability in first-price auctions. In Proceedings of the ACM Web Conference 2022. 58–67.
  10. Yang Cai and Constantinos Daskalakis. 2017. Learning Multi-item Auctions with (or without) Samples. In FOCS.
  11. Dynamic Budget Throttling in Repeated Second-Price Auctions. arXiv preprint arXiv:2207.04690 (2022).
  12. Pacing equilibrium in first price auction markets. Management Science 68, 12 (2022), 8515–8535.
  13. Multiplicative pacing equilibria in auction markets. Operations Research 70, 2 (2022), 963–989.
  14. Fairness in the Autobidding World with Machine-learned Advice. arXiv preprint arXiv:2209.04748 (2022).
  15. Multi-channel Autobidding with Budget and ROI Constraints. arXiv preprint arXiv:2302.01523 (2023).
  16. Efficiency of the first-price auction in the autobidding world. arXiv preprint arXiv:2208.10650 (2022).
  17. Oracle-Efficient Learning and Auction Design. In FOCS.
  18. Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator. The Annals of Mathematical Statistics (1956), 642–669.
  19. Budget Optimization in Search-Based Advertising Auctions. In Proceedings of the 8th ACM Conference on Electronic Commerce (San Diego, California, USA) (EC ’07). Association for Computing Machinery, New York, NY, USA, 40–49. https://doi.org/10.1145/1250910.1250917
  20. Advertisement Allocation for Generalized Second-Pricing Schemes. Operations Research Letters 38, 6 (Nov. 2010), 571–576.
  21. Bidding and pricing in budget and roi constrained markets. arXiv preprint arXiv:2107.07725 (2021).
  22. Dynamic incentive-aware learning: Robust pricing in contextual auctions. Operations Research 69, 1 (2021), 297–314.
  23. Incentive-aware contextual pricing with non-parametric market noise. (2019).
  24. Optimal no-regret learning in repeated first-price auctions. arXiv preprint arXiv:2003.09795 (2020).
  25. Kartik Hosanagar and Vadim Cherepanov. 2008. Optimal Bidding in Stochastic Budget Constrained Slot Auctions. In Proceedings of the 9th ACM Conference on Electronic Commerce (Chicago, Il, USA) (EC ’08). Association for Computing Machinery, New York, NY, USA, 20. https://doi.org/10.1145/1386790.1386794
  26. Yash Kanoria and Hamid Nazerzadeh. 2014. Dynamic Reserve Prices for Repeated Auctions: Learning from Bids. In Web and Internet Economics: 10th International Conference, WINE 2014, Beijing, China, December 14-17, 2014, Proceedings, Vol. 8877. Springer, 232.
  27. Optimizing Budget Constrained Spend in Search Advertising. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (Rome, Italy) (WSDM ’13). ACM, New York, NY, USA, 697–706. https://doi.org/10.1145/2433396.2433483
  28. Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets. arXiv preprint arXiv:2202.05881 (2022).
  29. Jean-Jacques Laffont and Jacques Robert. 1996. Optimal auction with financially constrained buyers. Economics Letters 52, 2 (1996), 181–186.
  30. Efficiency of non-truthful auctions under auto-bidding. arXiv preprint arXiv:2207.03630 (2022).
  31. Pascal Massart. 1990. The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality. The annals of Probability (1990), 1269–1283.
  32. Adwords and generalized online matching. J. ACM 54, 5 (2007).
  33. Mehryar Mohri and Andrés Munoz Medina. 2016. Learning algorithms for second-price auctions with reserve. The Journal of Machine Learning Research 17, 1 (2016), 2632–2656.
  34. Jamie Morgenstern and Tim Roughgarden. 2016. Learning Simple Auctions. In 29th Annual Conference on Learning Theory (Proceedings of Machine Learning Research, Vol. 49), Vitaly Feldman, Alexander Rakhlin, and Ohad Shamir (Eds.). PMLR, Columbia University, New York, New York, USA, 1298–1318.
  35. Mallesh M Pai and Rakesh Vohra. 2014. Optimal auctions with financially constrained buyers. Journal of Economic Theory 150 (2014), 383–425.
  36. Tim Roughgarden and Okke Schrijvers. 2016. Ironing in the dark. In Proceedings of the 2016 ACM Conference on Economics and Computation. 1–18.
  37. Paat Rusmevichientong and David P. Williamson. 2006. An Adaptive Algorithm for Selecting Profitable Keywords for Search-Based Advertising Services. In Proceedings of the 7th ACM Conference on Electronic Commerce (Ann Arbor, Michigan, USA) (EC ’06). Association for Computing Machinery, New York, NY, USA, 260–269. https://doi.org/10.1145/1134707.1134736
  38. Statista. 2021. Online advertising revenue in the United States from 2000 to 2021. https://www.statista.com/statistics/183816/us-online-advertising-revenue-since-2000/.
  39. Thomas Strömberg. 2009. A note on the differentiability of conjugate functions. Archiv der Mathematik 93 (2009), 481–485.
  40. Efficient regret bounds for online bid optimisation in budget-limited sponsored search auctions. In uai2014, 30th Conf. on Uncertainty in AI.
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