Multi-level Lasso for Sparse Multi-task Regression

By Aurelie C. Lozano, Grzegorz Swirszcz. 2012

103 citations recorded by the source corpus.

Subfields: Optimization & Training, Statistical Learning Theory

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Citations in the corpus

An Overview of Multi-task Learning (2018, background): “For example, in [12], a multi-level lassoisproposedbydecomposing w ji ,the( j , i )then-try in W , as w ji = θ j ˆ w ji .”
A Survey on Multi-Task Learning (2017, background): “s in a hierarchical structure, Han et al. [36] propose a multi-component product based decomposition for w ij where the number of components in the decomposition can be arbitrary instead of only 2 in [34], [35]. Similar to [34], Jebara [37], [38] proposes to learn a binary indicator vector to do mul”
Latent Multi-Task Architecture Learning (2017, background): “• The (cid:96) 1 /(cid:96) 2 group lasso regularizer is =1 || G 1 ,i,g || 2 , a weighted sum over the (cid:96) 2 norms of the groups, often used to enforce subspace sharing (Zhou, Jin, and Hoi 2010; ´ Swirszcz and Lozano 2012).”
Sluice networks: Learning what to share between loosely related tasks (2017, background): “Group Lasso The (cid:96) 1 /(cid:96) 2 group lasso regularizer is =1 || G 1 ,i,g || 2 , a weighted sum over the (cid:96) 2 norms of the groups, often used to enforce subspace sharing [33, 26].”
Multi-Task Learning and Algorithmic Stability (2015, methodology): “2007), learning the task relation from data in different ways (Jacob, Bach, and Vert 2008; Zhang and Yeung 2010a; Solnon, Arlot, and Bach 2012; Zhang and Yeung 2013; 2014), decomposition methods (Chen, Liu, and Ye 2010; Jalali et al. 2010; Lozano and Swirszcz 2012; Zweig and Weinshall 2013) which de”
Learning Multi-Level Task Groups in Multi-Task Learning (2015, background): “All rights reserved. and Xu 2010; Zhang and Schneider 2010; Zhang and Yeung 2010; Zhang 2013; Zhang and Yeung 2014), and some assume the task structure is hierarchical (Daumé III 2009; Jalali et al. 2010; Görnitz et al. 2011; Lozano and Swirszcz 2012; Zweig and Weinshall 2013).”
Multi-Task Multiple Kernel Relationship Learning (2016, methodology): “The motivation for this approach stems from multitask feature learning that learns joint feature representation shared across multiple tasks [1, 23].”
Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework (2014, methodology): “…the multi-task feature learning (MTFL) model (Liu, Ji, and Ye 2009), the dirty model (DM) (Jalali et al. 2010), the multilevel Lasso (MLL) model (Lozano and Swirszcz 2012), the tree-guided group Lasso (TGGL) model (Kim and Xing 2010), and the multi-task cascade (Cascade) model (Zweig and…”
On Multiplicative Multitask Feature Learning (2014, methodology): “which is used in Lozano and Swirszcz (2012). This problem is equivalent to the following joint regularization method”
Multi-task Learning (2020, background): “Swirszcz & Lozano (2012) and Bingel & Søgaard (2017) approximate multi-task performance by analyzing single-task learning characteristics.”