Online Contention Resolution Schemes (OCRSs) represent a modern tool for selecting a
subset of elements, subject to resource constraints, when the elements are presented to the …

Background. Our results tie into the literature on contention resolution schemes [3], which we
briefly define. Let P (𝐺) denote the fractional matching polytope of a graph 𝐺=(𝑉, 𝐸). In a …

In machine learning, continuously retraining a model guarantees accurate predictions based
on the latest data as training input. But to retrieve the latest data from a database, time …

Consider a network design application where we wish to lay down a minimum-cost
spanning tree in a given graph; however, we only have stochastic information about the …

Motivated by the growing interest in correlation-robust stochastic optimization, we
investigate stochastic selection problems beyond independence. Specifically, we consider …

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

A stochastic probing problem consists of a set of elements whose values are independent
random variables. The algorithm knows the distributions of these variables, but not the …

Suppose we are given a submodular function f over a set of elements, and we want to
maximize its value subject to certain constraints. Good approximation algorithms are known …

We consider the maximum bipartite matching problem in stochastic settings, namely the
query-commit and price-of-information models. In the query-commit model, an edge e …

Numerous recent papers have studied the tension between thickening and clearing a
market in (uncertain, online) long-time horizon Markovian settings. In particular,(Aouad and …