English

Feedback Control for Small Budget Pacing

Machine Learning 2026-03-10 v2 Computer Science and Game Theory

Abstract

Budget pacing is critical in online advertising to align spend with campaign goals under dynamic auctions. Existing pacing methods often rely on ad-hoc parameter tuning, which can be unstable and inefficient. We propose a principled controller that combines bucketized hysteresis with proportional feedback to provide stable and adaptive spend control. Our method provides a framework and analysis for parameter selection that enables accurate tracking of desired spend rates across campaigns. Experiments in real-world auctions demonstrate significant improvements in pacing accuracy and delivery consistency, reducing pacing error by 13% and λ\lambda-volatility by 54% compared to baseline method. By bridging control theory with advertising systems, our approach offers a scalable and reliable solution for budget pacing, with particular benefits for small-budget campaigns.

Keywords

Cite

@article{arxiv.2509.25429,
  title  = {Feedback Control for Small Budget Pacing},
  author = {Sreeja Apparaju and Yichuan Niu and Xixi Qi},
  journal= {arXiv preprint arXiv:2509.25429},
  year   = {2026}
}
R2 v1 2026-07-01T06:06:05.268Z