Ensuring that refugees and asylum seekers thrive (eg, find employment) in their host
countries is a profound humanitarian goal, and a primary driver of employment is the …

We consider the problem of fairly allocating sequentially arriving items to a set of individuals.
For this problem, the recently-introduced PACE algorithm leverages the dual averaging …

We study the fairness of dynamic resource allocation problem under the α-fairness criterion.
We recognize two different fairness objectives that naturally arise in this problem: the well …

Computing market equilibria is a problem of both theoretical and applied interest. Much
research to date focuses on the case of static Fisher markets with full information on buyers' …

We study a sequential resource allocation problem balancing fairness and efficiency for
nonprofit operations.(Un) fairness is measured by the expected maximum demand shortfall …

Online matching markets (OMMs) are commonly used in today's world to pair agents from
two parties (whom we will call offline and online agents) for mutual benefit. However, studies …

Fairness is a major concern in contemporary decision problems. In these situations, the
objective is to maximize fairness while preserving the efficacy of the underlying decision …

Recent studies successfully learned static graph embeddings that are structurally fair by
preventing the effectiveness disparity of high-and low-degree vertex groups in downstream …

We consider government service allocation--how the government allocates resources (eg,
maintenance of public infrastructure) over time. It is important to make these decisions …

This work addresses learning online fair division under uncertainty, where a central planner
sequentially allocates items without precise knowledge of agents' values or utilities …