- - 在通信的任意分区模型中的回归
Yi Li1, Honghao Lin2, David P Woodruff2
1Division of Mathematical Sciences Nanyang Technological University.
概括
这项研究利用随机化方法优化了分布式回归通信的复杂性. 新的边界显著改善了对最小平方和一般回归问题的先前工作.
科学领域:
- 分布式计算 分布式计算
- 信息理论是信息理论.
- 机器学习是机器学习.
背景情况:
- 分布式回归问题涉及一个协调器和多个服务器持有数据分区.
- 对于这个问题,现有的通信复杂度极限是不理想的,特别是对于一般的回归.
研究的目的:
- 在协调器模型中,为分布式回归问题推导出改进的随机通信复杂度边界.
- 建立最小平方回归的最佳边界,并为一般回归提供新的边界.
主要方法:
- 在分布式计算的协调模型中使用随机方法.
- 在数据被添加共享的任意分区模型中分析通信复杂性.
主要成果:
- 实现了分布式最小平方回归的O(log(1/δ)) 位的第一个最佳边界.
- 建立了一般分布式回归的O{n^{1/3)) 上限.
- 证明,对于大n,主要项依赖于n的线性,而不是二次性.
- 已证明的通讯下限为 Ω(n^(1/3)) 对于一般回归和 Ω(1) 对于最小平方.
结论:
- 新的边界代表了与现有结果相比的显著改进.
- 这些发现为分布式回归任务提供了更有效的通信协议.
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