Machine-learned predictors, although achieving very good results for inputs resembling
training data, cannot possibly provide perfect predictions in all situations. Still, decision …

We explore the fundamental problem of sorting through the lens of learning-augmented
algorithms, where algorithms can leverage possibly erroneous predictions to improve their …

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 …

Paging is a prototypical problem in the area of online algorithms. It has also played a central
role in the development of learning-augmented algorithms. Previous work on learning …

In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a
priori unknown processing requirements with the objective to minimize the total (weighted) …

Augmenting algorithms with learned predictions is a promising approach for going beyond
worst-case bounds. Dinitz, Im, Lavastida, Moseley, and Vassilvitskii~(2021) have …

Learning-augmented algorithms—in which, traditional algorithms are augmented with
machine-learned predictions—have emerged as a framework to go beyond worst-case …

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 …

The growing body of work in learning-augmented online algorithms studies how online
algorithms can be improved when given access to ML predictions about the future …

Priority queues are one of the most fundamental and widely used data structures in
computer science. Their primary objective is to efficiently support the insertion of new …