分布式多任务学习提升异构数据下的模型精度与泛化能力
Distributed Networked Multi-task Learning
- 节点按任务分组，通过有向网络通信，异步更新线性模型
- 引入组内局部与组间全局正则化，分别降低噪声与提升泛化性能
- 在温度预测与学生成绩建模中验证了方法的通用性与有效性
我们研究一种分布式多任务学习框架，用于处理具有异质性或相关性的多个线性模型估计任务。假设节点可按任务分组，并根据有向网络拓扑进行通信。每个节点异步估计线性模型，受组内局部正则化（降噪）和组间全局正则化（提升泛化）约束。本文给出了估计器收敛性与任务关系的有限时间刻画，并在两个实例中展示了该方法的广泛适用性：随机场温度估计和不同学区学生表现建模。
原文摘要 · Abstract (English)
We consider a distributed multi-task learning scheme that accounts for multiple linear model estimation tasks with heterogeneous and/or correlated data streams. We assume that nodes can be partitioned into groups corresponding to different learning tasks and communicate according to a directed network topology. Each node estimates a linear model asynchronously and is subject to local (within-group) regularization and global (across groups) regularization terms targeting noise reduction and generalization performance improvement respectively. We provide a finite-time characterization of convergence of the estimators and task relation and illustrate the scheme's general applicability in two examples: random field temperature estimation and modeling student performance from different academic districts.
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