# Contract Scheduling With Predictions

arXiv: https://arxiv.org/abs/2011.12439 · HTML: https://mlsanity.com/paper/2011.12439/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Contract Scheduling with Distributional and Multiple Advice](https://mlsanity.com/paper/2404.12485/) (2024)
- [Overcoming Brittleness in Pareto-Optimal Learning-Augmented Algorithms](https://mlsanity.com/paper/2408.04122/) (2024)
- [Learning-Augmented Online Bidding in Stochastic Settings](https://mlsanity.com/paper/2510.25582/) (2025)
- [Online Search With Best-Price and Query-Based Predictions](https://mlsanity.com/paper/2112.01592/) (2021)
- [Decision-Theoretic Approaches for Improved Learning-Augmented Algorithms](https://mlsanity.com/paper/2501.17701/) (2025)

## 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.