Crowdsourcing is a computing paradigm where humans are actively involved in a
computing task, especially for tasks that are intrinsically easier for humans than for …

Matching, capturing allocation of items to unit-demand buyers, or tasks to workers, or pairs of
collaborators, is a central problem in economics. Indeed, the growing prevalence of …

Matching is a classic problem with a rich history and a significant impact, both on the theory
of algorithms and in practice. Recently there has been a surge of interest in the online …

We consider a logit model-based framework for modeling joint pricing and assortment
decisions that take into account customer features. This model provides a significant …

We study online variants of weighted bipartite matching on graphs and hypergraphs. In our
model for online matching, the vertices on the right-hand side of a bipartite graph are given …

We propose a new model for augmenting algorithms with predictions by requiring that they
are formally learnable and instance robust. Learnability ensures that predictions can be …

We introduce the abstract problem of rounding an unknown fractional bipartite b-matching x
revealed online (eg, output by an online fractional algorithm), exposed node-by-node on …

Dynamic resource allocation problems arise under a variety of settings and have been
studied across disciplines such as operations research and computer science. The present …

Online bipartite matching is one of the most fundamental problems in the online algorithms
literature. Karp, Vazirani, and Vazirani (STOC 1990) gave an elegant algorithm for …

Motivated by applications from ride-sharing and kidney exchange, we study the problem of
matching agents who arrive at a marketplace over time and leave after d time periods …