Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings
subject to unanticipated challenges with complex social effects. Motivated by real-world …

In this paper, we study a class of two-stage robust binary optimization problems with
objective uncertainty, where recourse decisions are restricted to be mixed-binary. For these …

We study the problem of eliciting the preferences of a decision-maker through a moderate
number of pairwise comparison queries to make them a high quality recommendation for a …

Robust optimization is a popular paradigm for modeling and solving two-and multi-stage
decision-making problems affected by uncertainty. In many real-world applications, the time …

The K-adaptability problem is a special case of adaptive robust optimization with discrete
recourse that aims to prepare K solutions under uncertainty, and select among them upon …

We consider robust combinatorial optimization problems with cost uncertainty where the
decision maker can prepare K solutions beforehand and chooses the best of them once the …

We study robust solutions for combinatorial optimization problems with budgeted uncertainty
sets in the min–max–min setting, where the decision maker is allowed to choose a set of k …

We consider min-max-min optimization with smooth and strongly convex objectives. Our
motivation for studying this class of problems stems from its connection to the-center …

In this work we investigate the min-max-min robust optimization problem applied to
combinatorial problems with uncertain cost-vectors which are contained in a convex …

In this paper, we consider a variant of adaptive robust combinatorial optimization problems
where the decision maker can prepare K solutions and choose the best among them upon …