Learning-augmented priority queues

Conference item

Priority queues are one of the most fundamental and widely used data structures in computer science. Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priority element. In this study, we investigate the design of priority queues within the learning-augmented framework, where algorithms use potentially inaccurate predictions to enhance their worst-case performance. We examine three prediction models spanning different use cases, and we show how the predictions can be leveraged to enhance the performance of priority queue operations. Moreover, we demonstrate the optimality of our solution and discuss some possible applications.

Authors: Benomar, Z; Coester, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Neural Information Processing Systems Foundation
• Host title: Advances in Neural Information Processing Systems 37 (NeurIPS 2024)
• Pages: 124163-124197
• Series number: 37
• Publication date: 2025-02-01
• Acceptance date: 2024-09-25
• Event title: 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)
• Event location: Vancouver
• Event website: https://neurips.cc/Conferences/2024
• Event start date: 2024-12-10
• Event end date: 2024-12-15
• Language: English