BriefGPT.xyz
Feb, 2019
大规模学习分层互动:凸优化方法
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
HTML
PDF
Hussein Hazimeh, Rahul Mazumder
TL;DR
提出了一种基于凸松弛和近端梯度下降的高度可扩展算法,该算法采用了新的筛选规则和专门的激活集策略,能够处理具有密集设计矩阵的问题,并在预测和变量选择方面优于当前技术水平。
Abstract
In many learning settings, it is beneficial to augment the main features with
pairwise interactions
. Such interaction models can be often enhanced by performing
variable selection
under the so-called strong
→