Adaptive Bidding Policies for First-Price Auctions with Budget Constraints under Non-stationarity

[2505.02796] Adaptive Bidding Policies for First-Price Auctions with Budget Constraints under Non-stationarity Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Science and Game Theory arXiv:2505.02796 (cs) [Submitted on 5 May 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: Adaptive Bidding Policies for First-Price Auctions with Budget Constraints under Non-stationarity Authors: Yige Wang , Jiashuo Jiang View a PDF of the paper titled Adaptive Bidding Policies for First-Price Auctions with Budget Constraints under Non-stationarity, by Yige Wang and 1 other authors View PDF HTML (experimental) Abstract: We study how a budget-constrained bidder should learn to adaptively bid in repeated first-price auctions to maximize her cumulative payoff. This problem arose due to an industry-wide shift from second-price auctions to first-price auctions in display advertising recently, which renders truthful bidding (i.e., always bidding one's private value) no longer optimal. We propose a simple dual-gradient-descent-based bidding policy that maintains a dual variable for budget constraint as the bidder consumes her budget. In analysis, we consider two settings regarding the bidder's knowledge of her private values in the future: (i) an uninformative setting where all the distributional knowledge (can be non-stationary) is entirely unknown to the bidder, and (ii) an informative setting where a prediction of the budget allocation in advance. We characterize the performance loss (or regret) relative to an optimal policy with complete information on the stochasticity. For uninformative setting, We show that the regret is \tilde{O}(\sqrt{T}) plus a variation term that reflects the non-stationarity of the value distributions, and this is of optimal order. We then show that we can get rid of the variation term with the help of the prediction; specifically, the regret is \tilde{O}(\sqrt{T}) plus the prediction error term in the informative setting. Comments: Previously this version appeared as arXiv:2604.03103 which was submitted as a new work by accident Subjects: Computer Science and Game Theory (cs.GT) ; Machine Learning (cs.LG) Cite as: arXiv:2505.02796 [cs.GT] (or arXiv:2505.02796v2 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2505.02796 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yige Wang [ view email ] [v1] Mon, 5 May 2025 17:13:02 UTC (204 KB) [v2] Sat, 11 Apr 2026 05:42:23 UTC (1,049 KB) Full-text links: Access Paper: View a PDF of the paper titled Adaptive Bidding Policies for First-Price Auctions with Budget Constraints under Non-stationarity, by Yige Wang and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.GT < prev | next > new | recent | 2025-05 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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