Robust Budget Pacing with a Single Sample
Santiago Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian, Sivan, Di Wang

TL;DR
This paper demonstrates that in budget pacing for online advertising, a single sample per distribution suffices to achieve near-optimal regret, significantly reducing data requirements compared to previous methods.
Contribution
The paper introduces a novel approach showing that only one sample per distribution is needed for near-optimal budget pacing, improving over the previous $T ext{log} T$ sample requirement.
Findings
Achieves near-optimal $ ilde O(\sqrt{T})$ regret with one sample per distribution.
Robust to noise in sampling distributions.
Significantly reduces data requirements for budget pacing algorithms.
Abstract
Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inherent non-stationarity in an advertiser's value and also competing advertisers' values over time, a commonly used approach is to learn a target expenditure plan that specifies a target spend as a function of time, and then run a controller that tracks this plan. This raises the question: how many historical samples are required to learn a good expenditure plan? We study this question by considering an advertiser repeatedly participating in second-price auctions, where the tuple of her value and the highest competing bid is drawn from an unknown time-varying distribution. The advertiser seeks to maximize her total utility subject to her budget constraint. Prior work has shown the sufficiency of samples per distribution to achieve…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Auction Theory and Applications · Data Stream Mining Techniques
Methodstravel james
