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http://jmlr.org/papers/volume12/kolar11a/kolar11a.pdf
for the success of the support recovery. Using the model (3) and the estimation procedure in (4), we are able to identify regimes in which estimating the support is more efficient us ing the ordinary Lasso than with the multi-task Lasso and vice versa. Our results suggest that the multi-task Lasso
https://www.academia.edu/2683257/Union_support_recovery_in_multi-task_learning
Abstract We sharply characterize the performance of different penalization schemes for the problem of selecting the relevant variables in the multi-task setting. Previous work focuses on the regression problem where conditions on the design matrix
https://arxiv.org/abs/1008.5211
Title: Union Support Recovery in Multi-task Learning. Authors: Mladen Kolar, John Lafferty, Larry Wasserman (Submitted on 31 Aug 2010) Abstract: We sharply characterize the performance of different penalization schemes for the problem of selecting the relevant variables in the multi-task setting. Previous work focuses on the regression problem ...Author: Mladen Kolar, John Lafferty, Larry Wasserman
https://ece.duke.edu/~lcarin/Shaobo2.17.2012.pdf
Kolar et. al, 2011 Union Support Recovery in Multi-task Learning 4 / 21. Union Support Recovery The union support recovery problem can be understood as the generalization of variable selection to the group setting1. 1G. Obozinski, M. J. Wainwright, and M. I. Jordan, Support union recovery in
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.1624
union support recovery machine learning research multi-task learning simpler picture relevant variable simplified model different penalization scheme regression problem complex procedure design matrix multi-task setting
http://academictorrents.com/details/d5ec14d9c059a661009328f8e42a8fb8114a72ae
Union Support Recovery in Multi-task Learning Larry Wasserman and John Lafferty and Mladen Kolar
https://core.ac.uk/display/23873012
We sharply characterize the performance of different penalization schemes for the problem of selecting the relevant variables in the multi-task setting. Previous work focuses on the regression problem where conditions on the design matrix complicate the analysis. A clearer and simpler picture emerges by studying the Normal means model.
https://deepai.org/publication/gradient-surgery-for-multi-task-learning
Gradient Surgery for Multi-Task Learning. 01/19/2020 ∙ by Tianhe Yu, et al. ∙ 10 ∙ share . While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a …
https://www.researchgate.net/publication/1745560_Support_union_recovery_in_high-dimensional_multivariate_regression
Note in passing that the group Lasso has been used successfully in multi-task learning problems (with independent data) when a common sparsity structure is shared among the tasks Obozinski et al ...
https://zhuanlan.zhihu.com/p/27421983
[5] Zhou, J., Chen, J., & Ye, J. (2012) Multi-Task Learning , Theory, Algorithms, and Applications, SDM 往期内容推荐 深度学习模型-13 迁移学习（Transfer Learning）技术概述 <模型汇总-9> VAE基础：LVM、MAP、EM、MCMC、Variational Inference（VI） 《纯干货-6》Stanford University 2017年最新《Tensorflow与 ...
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