Contract Scheduling With Predictions
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Abstract (arXiv)
Contract scheduling is a general technique that allows to design a system with interruptible capabilities, given an algorithm that is not necessarily interruptible. Previous work on this topic has largely assumed that the interruption is a worst-case deadline that is unknown to the scheduler. In this work, we study the setting in which there is a potentially erroneous prediction concerning the interruption. Specifically, we consider the setting in which the prediction describes the time that the interruption occurs, as well as the setting in which the prediction is obtained as a response to a single or multiple binary queries. For both settings, we investigate tradeoffs between the robustness (i.e., the worst-case performance assuming adversarial prediction) and the consistency (i.e, the performance assuming that the prediction is error-free), both from the side of positive and negative results.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 2.
5 papers.
- 2024Contract Scheduling with Distributional and Multiple Advicecited by 8
- 2024Overcoming Brittleness in Pareto-Optimal Learning-Augmented Algorithmscited by 8
- 2025Learning-Augmented Online Bidding in Stochastic Settingscited by 3
- 2021Online Search With Best-Price and Query-Based Predictionscited by 2
- 2025Decision-Theoretic Approaches for Improved Learning-Augmented Algorithmscited by 0
- 2025Learning-Augmented Online Bidding in Stochastic Settings29 Oct 2025
- 2025Decision-Theoretic Approaches for Improved Learning-Augmented Algorithms29 Jan 2025
- 2024Overcoming Brittleness in Pareto-Optimal Learning-Augmented Algorithms7 Aug 2024
- 2024Contract Scheduling with Distributional and Multiple Advice18 Apr 2024
- 2021Online Search With Best-Price and Query-Based Predictions2 Dec 2021