Adaptive Multi-Task Lasso: with Application to eQTL Detection

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A Survey on Multi-Task Learning (2017, methodology): “Moreover, multi-task feature selection methods based on the `2,1 norm such as [24], [25], [26], [27] can be reformulated as instances of problem (32) by using an alternative form as”
Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance (2011, background): “1This paper was first submitted to a conference in 2010 and we notice a similar objective in [12] when we prepare the camera-ready draft of this paper.”
On Multiplicative Multitask Feature Learning (2014, methodology): “These methods employ a regularizer based on the so-called (cid:96) 1 ,p matrix norm [12, 13, 15, 22, 24] that is the sum of the (cid:96) p norms of the rows in a matrix.”
Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, methodology): “In [108] and [109], a new multitask feature selection method based on MTFL is proposed.”
Flexible Clustered Multi-Task Learning by Learning Representative Tasks (2016, background): “The new approach allows an arbitrary task to be described by multiple representative tasks, effectively soft-assigning a task to multiple clusters with different weights.”
Multi-level Lasso for Sparse Multi-task Regression (2012, background): “Another noteworthy approach considers an adaptive multipleoutput Lasso formulation, where mixture weight are introduced over the features (Lee et al., 2010) that are estimated via a Bayesian framework.”