This paper considers online convex optimization with hard constraints and analyzes
achievable regret and cumulative hard constraint violation (violation for short). The problem …

Large-scale, two-sided matching platforms must find market outcomes that align with user
preferences while simultaneously learning these preferences from data. But since …

We design decentralized algorithms for regret minimization in the two sided matching market
with one-sided bandit feedback that significantly improves upon the prior works (Liu et al …

Queueing systems are widely applicable stochastic models with use cases in
communication networks, healthcare, service systems, etc. Although their optimal control …

Employment outcomes of resettled refugees depend strongly on where they are placed
inside the host country. While the US sets refugee capacities for communities on an annual …

Nonprofit crowdsourcing platforms such as food recovery organizations rely on volunteers to
perform time-sensitive tasks. Thus, their success crucially depends on efficient volunteer …

We review the role of information and learning in the stability and optimization of queueing
systems. In recent years, techniques from supervised learning, online learning, and …

The choice of care unit upon admission to the hospital is a challenging task because of the
wide variety of patient characteristics, uncertain needs of patients, and limited number of …

Successful content moderation in online platforms relies on a human-AI collaboration
approach. A typical heuristic estimates the expected harmfulness of a post and uses fixed …

This paper proposes and evaluates a new hierarchical approach to peer-to-peer logistics
platforms, recasting the platform's role as one providing personalized menus of requests to …