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About this paper
Bhuvesh Kumar, Jamie Morgenstern, Okke Schrijvers
Researchers developed an end-to-end budget management system for online ad campaigns that provides the first learning-theoretic guarantees for non-stationary market conditions. The system offers provable bounds on the accuracy of spend plan estimation and demonstrates vanishing regret for its pacing algorithm, outperforming existing methods in simulated environments.
Key insights
- Optimal bidding strategies in budget-constrained ad campaigns can be characterized by a fixed multiplicative shading factor, enabling problem decomposition through duality theory.
- Achieving vanishing regret in non-stationary environments for budget pacing necessitates the use of historical data, demonstrating that purely online or static pacing strategies are insufficient.
- The accuracy of estimating optimal spend rates is significantly influenced by the statistical properties of underlying market distributions and the chosen estimation techniques, such as KDE for prices.
Summary by alphaXiv.
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