34 citations · 78 across the 11 of their papers we have counts for
12 papers
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
Yizhou Zhang, Guannan Qu, Pan Xu +3
We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of thei…
Group-level Fairness Maximization in Online Bipartite Matching
Will Ma, Pan Xu, Yifan Xu
We consider the allocation of limited resources to heterogeneous customers who arrive in an online fashion. We would like to allocate the resources "fairly", so that no group of cu…
Improved Approximation Algorithms for Stochastic-Matching Problems
Marek Adamczyk, Brian Brubach, Fabrizio Grandoni +3
We consider the Stochastic Matching problem, which is motivated by applications in kidney exchange and online dating. In this problem, we are given an undirected graph. Each edge i…
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman +2
Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that d…
Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity
John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan +1
In bipartite matching problems, vertices on one side of a bipartite graph are paired with those on the other. In its online variant, one side of the graph is available offline, whi…
A PTAS for a Class of Stochastic Dynamic Programs
Hao Fu, Jian Li, Pan Xu
We develop a framework for obtaining polynomial time approximation schemes (PTAS) for a class of stochastic dynamic programs. Using our framework, we obtain the first PTAS for the…