Robust Budget Pacing with a Single Sample

• URL: http://arxiv.org/abs/2302.02006v1
• Date: Fri, 3 Feb 2023 21:23:09 GMT
• Title: Robust Budget Pacing with a Single Sample
• Authors: Santiago Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
• Abstract summary: We show that just one sample per distribution is enough to achieve the near-optimal $tilde O(sqrtT)$-regret. We show that just one sample per distribution is enough to achieve the near-optimal $tilde O(sqrtT)$-regret.
• Score: 9.826939499674676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $T$ 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 $T\log T$ samples per distribution to achieve the optimal $O(\sqrt{T})$-regret. We dramatically improve this state-of-the-art and show that just one sample per distribution is enough to achieve the near-optimal $\tilde O(\sqrt{T})$-regret, while still being robust to noise in the sampling distributions.

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