Online Bidding under RoS Constraints without Knowing the Value
Name
3696410.3714734.pdf
Size
1.28 MB
Format
Adobe PDF
Checksum (MD5)
a3117b4457cc9cb19d05a87420df8409
Author(s)
Vijayan, Sushant
Feng, Zhe
Padmanabhan, Swati
Shanmugam, Karthikeyan
Suggala, Arun
Wang, Di
Date Issued
April 22, 2025
Publisher
ACM|Proceedings of the ACM Web Conference 2025
Citation
Sushant Vijayan, Zhe Feng, Swati Padmanabhan, Karthikeyan Shanmugam, Arun Suggala, and Di Wang. 2025. Online Bidding under RoS Constraints without Knowing the Value. In Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 3096–3107.
Version
Final published version
Abstract
We consider the problem of bidding in online advertising, where an advertiser aims to maximize value while adhering to budget and Return-on-Spend (RoS) constraints. Unlike prior work that assumes knowledge of the value generated by winning each impression (e.g., conversions), we address the more realistic setting where the advertiser must simultaneously learn the optimal bidding strategy and the value of each impression opportunity. This introduces a challenging exploration-exploitation dilemma: the advertiser must balance exploring different bids to estimate impression values with exploiting current knowledge to bid effectively. To address this, we propose a novel Upper Confidence Bound (UCB)-style algorithm that carefully manages this trade-off. Via a rigorous theoretical analysis, we prove that our algorithm achieves Õ(₲T log(|B|T) ) regret and constraint violation, where T is the number of bidding rounds and B is the domain of possible bids. This establishes the first optimal regret and constraint violation bounds for bidding in the online setting with unknown impression values. Moreover, our algorithm is computationally efficient and simple to implement. We validate our theoretical findings through experiments on synthetic data, demonstrating that our algorithm exhibits strong empirical performance compared to existing approaches.
Description
WWW ’25, April 28–May 2, 2025, Sydney, NSW, Australia.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3696410.3714734
