12 citations · 17 across the 7 of their papers we have counts for
8 papers
Exploring the Tradeoff between Competitive Ratio and Variance in Online-Matching Markets
Pan Xu
In this paper, we propose an online-matching-based model to study the assignment problems arising in a wide range of online-matching markets, including online recommendations, ride…
Online minimum matching with uniform metric and random arrivals
Sharmila Duppala, Karthik A. Sankararaman, Pan Xu
We consider Online Minimum Bipartite Matching under the uniform metric. We show that Randomized Greedy achieves a competitive ratio equal to , which matches th…
Equity Promotion in Online Resource Allocation
Pan Xu, Yifan Xu
We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a governmen…
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
Yue Wu, Tao Jin, Hao Lou +3
In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items. Thus a uniform querying strategy over users may not be optima…
Fairness Maximization among Offline Agents in Online-Matching Markets
Will Ma, Pan Xu, Yifan Xu
Matching markets involve heterogeneous agents (typically from two parties) who are paired for mutual benefit. During the last decade, matching markets have emerged and grown rapidl…
Trading the System Efficiency for the Income Equality of Drivers in Rideshare
Yifan Xu, Pan Xu
Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we stud…
A Unified Model for the Two-stage Offline-then-Online Resource Allocation
Yifan Xu, Pan Xu, Jianping Pan +1
With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agen…
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare
Michael J. Curry, John P. Dickerson, Karthik Abinav Sankararaman +3
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests. We model the dispatching process in rideshare as a Markov chain that takes into ac…