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 …

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 …

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

As power management has become a primary concern in modern data centers, computing
resources are being scaled dynamically to minimize energy consumption. We initiate the …

We consider the problem of convex function chasing with black-box advice, where an online
decision-maker aims to minimize the total cost of making and switching between decisions …

An important goal of modern scheduling systems is to efficiently manage power usage. In
energy-efficient scheduling, the operating system controls the speed at which a machine is …

This paper leverages machine-learned predictions to design competitive algorithms for
online conversion problems with the goal of improving the competitive ratio when …

The design of online algorithms has tended to focus on algorithms with worst-case
guarantees, eg, bounds on the competitive ratio. However, it is well-known that such …

We study the problem of augmenting online algorithms with machine learned (ML) advice. In
particular, we consider the\emph {multi-shop ski rental}(MSSR) problem, which is a …

Despite several calls from the community for improving the sustainability of computing,
sufficient progress is yet to be made on one of the key prerequisites of sustainable …