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Multi-Platform Autobidding with and without Predictions
Conference proceeding   Open access

Multi-Platform Autobidding with and without Predictions

Gagan Aggarwal, Anupam Gupta, Xizhi Tan and Mingfei Zhao
Proceedings of the ACM on Web Conference 2025, pp 2850-2859
28 Apr 2025
DOI: https://doi.org/10.1145/3696410.3714936
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1145/3696410.3714936View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0,  Open

Abstract

Theory of computation -- Design and analysis of algorithms
We study the problem of finding the optimal bidding strategy for an advertiser in a multi-platform auction setting. The competition on a platform is captured by a value and a cost function, mapping bidding strategies to value and cost respectively. We assume a diminishing returns property, whereby the marginal cost is increasing in value. The advertiser uses an autobidder that selects a bidding strategy for each platform, aiming to maximize total value subject to budget and return-on-spend constraint. The advertiser has no prior information and learns about the value and cost functions by querying a platform with a specific bidding strategy. Our goal is to design algorithms that find the optimal bidding strategy with a small number of queries. We first present an algorithm that requires (O(m log (mn) log n)) queries, where m is the number of platforms and n is the number of possible bidding strategies in each platform. Moreover, we adopt the learning-augmented framework and propose an algorithm that utilizes a (possibly erroneous) prediction of the optimal bidding strategy. We provide a O(m log (mη) log η) query-complexity bound on our algorithm as a function of the prediction error η. This guarantee gracefully degrades to (O(m log (mn) log n)). This achieves a "best-of-both-worlds" scenario: (O(m)) queries when given a correct prediction, and (O(m log (mn) log n)) even for an arbitrary incorrect prediction.

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Computer Science, Interdisciplinary Applications
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