Contract Scheduling With Predictions
Spyros Angelopoulos, Shahin Kamali
Abstract
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.
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Cited by top-tier papers4
- Online Search with Best-Price and Query-Based PredictionsSpyros Angelopoulos, Shahin Kamali, Dehou ZhangAAAI 2022 · 11 citations
- Overcoming Brittleness in Pareto-Optimal Learning Augmented AlgorithmsAlex Elenter, Spyros Angelopoulos, Christoph Dürr, Yanni LefkiNeurIPS 2024 · 10 citations
- Learning-Augmented Online Bidding in Stochastic SettingsSpyros Angelopoulos, Bertrand SimonNeurIPS 2025 · 6 citations
- Decision-Theoretic Approaches for Improved Learning-Augmented AlgorithmsSpyros Angelopoulos, Christoph Dürr, Georgii MelidiICLR 2026 · 2 citations
Builds on3
- Online metric algorithms with untrusted predictionsAntonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak et al.ICML 2020 · 170 citations
- Near-Optimal Bounds for Online Caching with Machine Learned AdviceDhruv RohatgiSODA 2020 · 88 citations
- Online Scheduling via Learned WeightsSilvio Lattanzi, Thomas Lavastida, Benjamin Moseley, Sergei VassilvitskiiSODA 2020 · 83 citations
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