Distributed Multitask Learning

10/02/2015
by   Jialei Wang, et al.
0

We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space,where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/07/2016

Distributed Multi-Task Learning with Shared Representation

We study the problem of distributed multi-task learning with shared repr...
research
11/03/2021

Multi-task Learning of Order-Consistent Causal Graphs

We consider the problem of discovering K related Gaussian directed acycl...
research
09/26/2013

High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning

Joint sparsity regularization in multi-task learning has attracted much ...
research
07/03/2022

Saliency-Regularized Deep Multi-Task Learning

Multitask learning is a framework that enforces multiple learning tasks ...
research
10/18/2021

A Bayesian approach to multi-task learning with network lasso

Network lasso is a method for solving a multi-task learning problem thro...
research
05/21/2022

Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Gaussian graphical regression is a powerful means that regresses the pre...
research
04/02/2020

Distributed Primal-Dual Optimization for Online Multi-Task Learning

Conventional online multi-task learning algorithms suffer from two criti...

Please sign up or login with your details

Forgot password? Click here to reset