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 …

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 subsets of uncertain values, we study the problem of identifying the subset of
minimum total value (sum of the uncertain values) by querying as few values as possible …

In computing with explorable uncertainty, one considers problems where the values of some
input elements are uncertain, typically represented as intervals, but can be obtained using …

Matching theory is among the most fundamental graph optimization problems. Several
different variants of this problem have been introduced and studied. Maximum matching …

We study a stochastic variant of monotone submodular maximization problem as follows. We
are given a monotone submodular function as an objective function and a feasible domain …

When solving optimization problems that arise in real-world applications, uncertainty in the
input data and incomplete information are major challenges. Consider for example varying …