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Communication Dans Un Congrès Année : 2023

Advice Querying under Budget Constraint for Online Algorithms

Résumé

Several problems have been extensively studied in the learning-augmented setting, where the algorithm has access to some, possibly incorrect, predictions. However, it is assumed in most works that the predictions are provided to the algorithm as input, with no constraint on their size. In this paper, we consider algorithms with access to a limited number of predictions, that they can request at any time during their execution. We study three classical problems in competitive analysis, the ski rental problem, the secretary problem, and the non-clairvoyant job scheduling. We address the question of when to query predictions and how to use them.
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Dates et versions

hal-04420360 , version 1 (26-01-2024)

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  • HAL Id : hal-04420360 , version 1

Citer

Ziyad Benomar, Vianney Perchet. Advice Querying under Budget Constraint for Online Algorithms. NeurIPS 2023 - 37th Conference on Neural Information Processing Systems, Dec 2023, New Orleans, United States. ⟨hal-04420360⟩
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