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 …

Online bipartite graph matching is attracting growing research attention due to the
development of dynamic task assignment in sharing economy applications, where tasks …

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 …

We provide prophet inequality algorithms for online weighted matching in general (non-
bipartite) graphs, under two well-studied arrival models, namely edge arrival and vertex …

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 …

With the rapid development of smartphones, spatial crowdsourcing platforms are getting
popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to …

The online matching problem was introduced by Karp, Vazirani and Vazirani nearly three
decades ago. In that seminal work, they studied this problem in bipartite graphs with vertices …

Motivated by centralized matching markets, we study an online stochastic matching problem
on edge-weighted graphs, where the agents' arrivals and abandonments are stochastic and …

We introduce a weighted version of the ranking algorithm by Karp et al.(STOC 1990), and
we prove a competitive ratio of 0.6534 for the vertex-weighted online bipartite matching …

Three decades ago, Karp, Vazirani, and Vazirani (STOC 1990) defined the online matching
problem and gave an optimal (1-1/e)-competitive (about 0.632) algorithm. Fifteen years later …