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 …

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 …

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 …

In the single-machine non-clairvoyant scheduling problem, the goal is to minimize the total
completion time of jobs whose processing times are unknown a priori. We revisit this well …

In this work we introduce an alternative model for the design and analysis of strategyproof
mechanisms that is motivated by the recent surge of work in" learning-augmented …

We propose a new model for augmenting algorithms with predictions by requiring that they
are formally learnable and instance robust. Learnability ensures that predictions can be …