Abstract The Minimum Spanning Tree problem aims to create a subset of a graph where all
the vertices are connected with the minimum edge weights and with no cycle. In this field, an …

We study how to utilize (possibly erroneous) predictions in a model for computing under
uncertainty in which an algorithm can query unknown data. Our aim is to minimize the …

We introduce a novel adversarial model for scheduling with explorable uncertainty. In this
model, the processing time of a job can potentially be reduced (by an a priori unknown …

We study the problem of sorting under incomplete information, when queries are used to
resolve uncertainties. Each of n data items has an unknown value, which is known to lie in a …

Learning-augmented algorithms have been attracting increasing interest, but have only
recently been considered in the setting of explorable uncertainty where precise values of …

Given a hypergraph with uncertain node weights following known probability distributions,
we study the problem of querying as few nodes as possible until the identity of a node with …

We study problems with stochastic uncertainty information on intervals for which the precise
value can be queried by paying a cost. The goal is to devise an adaptive decision tree to find …

Given a nominal combinatorial optimization problem, we consider a robust two-stages
variant with polyhedral cost uncertainty, called Decision-Dependent Information Discovery …

Combinatorial optimization captures a wide range of applications such as infrastructure
design and scheduling of tasks. For a fixed number of possible new infrastructure …

This article studies the Minimum Spanning Tree Problem under Explorable Uncertainty as
well as a related vertex uncertainty version of the problem. We particularly consider special …