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 …

We consider online allocation problems with concave revenue functions and resource
constraints, which are central problems in revenue management and online advertising. In …

The extension of classical online algorithms when provided with predictions is a new and
active research area. In this paper, we extend the primal-dual method for online algorithms …

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 …

How does a search engine company decide what ads to display with each query so as to
maximize its revenue? This turns out to be a generalization of the online bipartite matching …

We explore the problem of assigning heterogeneous tasks to workers with different,
unknown skill sets in crowdsourcing markets such as Amazon Mechanical Turk. We first …

The primal–dual method is a powerful algorithmic technique that has proved to be extremely
useful for a wide variety of problems in the area of approximation algorithms for NP-hard …

Crowdsourcing markets have gained popularity as a tool for inexpensively collecting data
from diverse populations of workers. Classification tasks, in which workers provide labels …

We consider the problem of a search engine trying to assign a sequence of search keywords
to a set of competing bidders, each with a daily spending limit. The goal is to maximize the …

We study the online stochastic bipartite matching problem, in a form motivated by display ad
allocation on the Internet. In the online, but adversarial case, the celebrated result of Karp …