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.

翻译：匹配问题在研究界得到了广泛研究，特别是具有众多应用的广告拍卖问题，其应用范围从网络设计到广告领域。随着机器学习领域的各种进展，一个自然的问题是经典算法能否从机器学习中受益并获得更优质量的解。即使在匹配问题中实现很小比例的性能提升，也能为所研究的用例带来显著增益。例如，网络吞吐量或广告拍卖收入可大幅提高。本文提出了一种基于机器学习预测的算法，用于解决在线有界分配和在线广告拍卖问题。我们构建了原对偶算法，其竞争性能取决于预测的质量。当预测准确时，算法的性能超越了先前的性能界限；而当预测具有误导性时，算法仍能保持标准的悲观情况性能保证。我们基于生成数据进行了支持性实验，以验证我们的理论发现。