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 …

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 …

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

We consider the question of speeding up classic graph algorithms with machine-learned
predictions. In this model, algorithms are furnished with extra advice learned from past or …

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

We study the problem of improving the performance of online algorithms by incorporating
machine-learned predictions. The goal is to design algorithms that are both consistent and …

The secretary problem is probably the purest model of decision making under uncertainty. In
this paper we ask which advice can we give the algorithm to improve its success probability …

There has been recent interest in using machine-learned predictions to improve the worst-
case guarantees of online algorithms. In this paper we continue this line of work by studying …

This paper studies online algorithms augmented with multiple machine-learned predictions.
We give a generic algorithmic framework for online covering problems with multiple …