主题:Two-Sided Capacitated Submodular Maximization in Gig Platforms
报告人：美国新泽西理工，许攀，助理教授
报告时间：2023年12月6日（周三）上午11:00
报告地点：计算机楼142
主办单位：东南大学网络空间安全学院
承办单位：江苏省网络空间安全学会
报告简介：
In this talk, he will present two generic models of capacitated coverage maximization to study task-worker assignment problems in various gig economy platforms. The proposed models incorporate the following features: (1) Each task and worker can have an arbitrary matching capacity, which captures the limited number of copies or finite budget for the task and the working capacity of the worker; (2) Each task is associated with a coverage utility function. The objective is to design an allocation policy that maximizes the sum of all tasks' utilities, subject to capacity constraints on tasks and workers. Two settings are considered: offline, where all tasks and workers are static, and online, where tasks are static while workers arrive dynamically. He will present two LP-based rounding algorithms that achieve an optimal approximation ratio of 1−1/e~0.632 for offline coverage maximization and a competitive ratio of (19−67/𝖾^3)/27~0.580 for online coverage maximization, respectively.
个人简介：
Pan Xu is currently an Assistant Professor in the Department of Computer Science at New Jersey Institute of Technology. His research interests broadly span the intersection of Algorithms, Operations Research, and Artificial Intelligence. Recently, he has focused on designing and analyzing algorithms for offline and online matching models and applying them in various real-world matching markets, including crowdsourcing marketplaces, ridesharing platforms, and online recommendation systems.
