https://www.uni-bremen.de/en/cslog/nmegow

AG 1   Info

AG Audience

English

60 Minutes

Saarbrücken

Uncertainty poses a significant challenge on scheduling and planning tasks, where jobs may have unknown processing times or machines run at unknown speeds. However, assuming a complete lack of a priori information is overly pessimistic. With the rise of machine-learning methods and data-driven applications, access to predictions about input data or algorithmic actions becomes feasible. Yet, blindly trusting these predictions might lead to very poor solutions, due to the absence of quality guarantees.

In this talk, we explore recent advancements in the popular framework of Algorithms with Predictions, which integrates such error-prone predictions into online algorithm design. We examine various prediction models and error measures, showcasing learning-augmented algorithms for non-clairvoyant scheduling with strong error-dependent performance
guarantees. We demonstrate the potential of imperfect predictions to enhance scheduling efficiency and address uncertainty in real-world scenarios.

Nidhi Rathi

+49 681 9325 1134

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