Algorithms with predictions Page 1 JULY 2022 | VOL. 65 | NO. 7 | COMMUNICATIONS OF
THE ACM 33 viewpoints IMA GE B Y ANDRIJ BOR YS A SSOCIA TE S, USING SHUTTERS T …

Traditional online algorithms encapsulate decision making under uncertainty, and give ways
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …

Online algorithms are a hallmark of worst case optimization under uncertainty. On the other
hand, in practice, the input is often far from worst case, and has some predictable …

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

The extension of classical online algorithms when provided with predictions is a new and
active research area. In this paper, we extend the primal-dual method for online algorithms …

In the model of online caching with machine learned advice, introduced by Lykouris and
Vassilvitskii, the goal is to solve the caching problem with an online algorithm that has …

The classical analysis of online algorithms, due to its worst-case nature, can be quite
pessimistic when the input instance at hand is far from worst-case. Often this is not an issue …

We study the use of predictions by multiple experts (such as machine learning algorithms) to
improve the performance of online algorithms. In particular, we consider the classical rent-or …

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

We examine the problem of designing learning-augmented algorithms for metrical task
systems (MTS) that exploit machine-learned advice while maintaining rigorous, worst-case …