Learning Proportionally Fair Allocations with Low Regret

被引：0

作者：

Talebi M.S. ^{[1
]}

Proutiere A. ^{[1
]}

机构：

[1] EECS, KTH Royal Institute of Technology, Stockholm

来源：

| 2018年 / Association for Computing Machinery, 2 Penn Plaza, Suite 701, New York, NY 10121-0701, United States卷 / 46期

关键词：

proportional fairness; regret minimization; resource allocation; stochastic multi-armed bandits;

D O I：

10.1145/3219617.3219638

中图分类号：

学科分类号：

摘要：

We address the problem of learning Proportionally Fair (PF) allocations in parallel server systems with unknown service rates. We provide the first algorithms, to our knowledge, for learning such allocations with sub-linear regret. © 2018 ACM.

引用

页码：50 / 52

页数：2

共 50 条

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