activity
20192021
most citedTrading the System Efficiency for the Income Equality of Drivers in Rideshare

12 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2021

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…

cs.GT20213 cited

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…

cs.AI202012 cited

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…

cs.AI20202 cited

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…

cs.DS2019

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…