STA / BST 290 Seminar Series
Monday, January 5, 4:10pm, MSB 1143 (Statistics Seminar Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)
Speaker: Yuekai Sun (Stanford University)
Title: “A one-shot approach to distributed sparse regression”
Abstract: Modern massive datasets are usually not stored centrally, but distributed across machines connected by a network. The main computational challenge in a distributed setting is harnessing the computational capabilities of all the machines while keeping communication costs low. We focus on the high-dimensional regression problem and devise an approach that requires only a single round of communication among the machines. The main idea is to average "debiased'' lasso estimates. We show the approach recovers the convergence rate of the lasso as long as each machine has access to an adequate number of samples.