Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems
This work addresses matching problems like ad-auctions, offering incremental improvements that could enhance network throughput or revenue in applications such as advertising.
The paper tackles online bounded allocation and ad-auctions problems by developing primal-dual algorithms that incorporate machine learning predictions to improve performance, achieving better bounds with accurate predictions while maintaining worst-case guarantees when predictions are poor.
Matching problems have been widely studied in the research community, especially Ad-Auctions with many applications ranging from network design to advertising. Following the various advancements in machine learning, one natural question is whether classical algorithms can benefit from machine learning and obtain better-quality solutions. Even a small percentage of performance improvement in matching problems could result in significant gains for the studied use cases. For example, the network throughput or the revenue of Ad-Auctions can increase remarkably. This paper presents algorithms with machine learning predictions for the Online Bounded Allocation and the Online Ad-Auctions problems. We constructed primal-dual algorithms that achieve competitive performance depending on the quality of the predictions. When the predictions are accurate, the algorithms' performance surpasses previous performance bounds, while when the predictions are misleading, the algorithms maintain standard worst-case performance guarantees. We provide supporting experiments on generated data for our theoretical findings.