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

We study\emph {smoothed online convex optimization}, a version of online convex
optimization where the learner incurs a penalty for changing her actions between rounds …

We study online convex optimization in a setting where the learner seeks to minimize the
sum of a per-round hitting cost and a movement cost which is incurred when changing …

We consider online convex optimization (OCO) problems with switching costs and noisy
predictions. While the design of online algorithms for OCO problems has received …

In the chasing convex bodies problem, an online player receives a request sequence of N
convex sets K 1,…, KN contained in a normed space X of dimension d. The player starts at x …

This paper presents competitive algorithms for a novel class of online optimization problems
with memory. We consider a setting where the learner seeks to minimize the sum of a hitting …

We study online optimization in a setting where an online learner seeks to optimize a per-
round hitting cost, which may be non-convex, while incurring a movement cost when …

This article studies the fundamental problem of graph coloring in fully dynamic graphs. Since
the problem of computing an optimal coloring, or even approximating it to within n1-ε for any …

In this paper, we revisit the problem of smoothed online learning, in which the online learner
suffers both a hitting cost and a switching cost, and target two performance metrics …

Many real-world control systems, such as the smart grid and software defined networks,
have decentralized components that react quickly using local information and centralized …