The Pandora's Box problem and its extensions capture optimization problems with
stochastic input where the algorithm can obtain instantiations of input random variables at …

Our main contribution is a general framework to design efficient polynomial time
approximation schemes (EPTAS) for fundamental stochastic combinatorial optimization …

Sequential testing problems involve a complex system with several components, each of
which is “working” with some independent probability. The outcome of each component can …

In this paper we look at $ k $-stroll, point-to-point orienteering, as well as the deadline TSP
problem on graphs with bounded doubling dimension and bounded treewidth and present …

Stochastic optimization is a widely used approach for optimization under uncertainty, where
uncertain input parameters are modeled by random variables. Exact or approximation …

We study the classic problem of minimizing the expected total completion time of jobs on m
identical machines in the setting where the sizes of the jobs are stochastic. Specifically, the …

We consider the {\em correlated knapsack orienteering}(CSKO) problem: we are given a
travel budget $ B $, processing-time budget $ W $, finite metric space $(V, d) $ with root …

We consider the informative path planning (IPP) problem in which a robot interacts with an
uncertain environment and gathers information by visiting locations. The goal is to minimize …

Restless Multi-Armed Bandits (RMABs) offer a powerful framework for solving resource
constrained maximization problems. However, the formulation can be inappropriate for …

We consider sequential testing problems that involve a system of n stochastic components,
each of which is either working or faulty with independent probability. The overall state of the …