Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems

Abstract

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.
Fichier principal
Vignette du fichier
ALT_2023.pdf (359.69 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Licence

Dates and versions

hal-03997203 , version 1 (27-02-2023)

Licence

Identifiers

  • HAL Id : hal-03997203 , version 1

Cite

Nguyễn Kim Thắng, Enikő Kevi. Primal-Dual Algorithms with Predictions for Online Bounded Allocation and Ad-Auctions Problems. ALT 2023 - 34th International Conference on Algorithmic Learning Theory, Feb 2023, Singapore, Singapore. pp.1-18. ⟨hal-03997203⟩
72 View
58 Download

Share

Gmail Mastodon Facebook X LinkedIn More