Online algorithms with predictions is a popular and elegant framework for bypassing
pessimistic lower bounds in competitive analysis. In this model, online algorithms are …

We consider classic clustering problems in fully dynamic data streams, where data elements
can be both inserted and deleted. In this context, several parameters are of importance:(1) …

We introduce a novel measure for quantifying the error in input predictions. The error is
based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a …

Following the research agenda initiated by Munoz & Vassilvitskii [1] and Lykouris &
Vassilvitskii [2] on learning-augmented online algorithms for classical online optimization …

In the online and incremental variants of Facility Location, the demands arrive one-by-one
and must be assigned to an open facility upon arrival, without any knowledge about future …

We study the problem of leasing facilities over time, following the general infrastructure
leasing problem framework introduced by Anthony and Gupta (2007). If there are K different …

We study the online facility location problem with uniform facility costs in the random-order
model. Meyerson's algorithm [FOCS'01] is arguably the most natural and simple online …

In recent years, there has been an increasing focus on designing online algorithms with
(machine-learned) predictions. The ideal learning-augmented algorithm is comparable to …

In this paper we study three previously unstudied variants of the online Facility Location
problem, considering an intrinsic scenario when the clients and facilities are not only …

We consider online optimization problems in which certain goods have to be acquired in
order to provide a service or infrastructure. Classically, decisions for such problems are …