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cs.LG2023
Mixing predictions for online metric algorithms
Antonios Antoniadis, Christian Coester, Marek Eliáš +2
A major technique in learning-augmented online algorithms is combining multiple algorithms or predictors. Since the performance of each predictor may vary over time, it is desirabl…
cs.LG2021
Double Coverage with Machine-Learned Advice
Alexander Lindermayr, Nicole Megow, Bertrand Simon
We study the fundamental online k-server problem in a learning-augmented setting. While in the traditional online model, an algorithm has no information about the request sequence,…