Hierarchical Regularization Cascade for Joint Learning

By Alon Zweig, Daphna Weinshall. 2013

50 citations recorded by the source corpus.

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Citations in the corpus

An Overview of Multi-task Learning (2018, background): “In [59], each component matrix is assumed to be jointly sparse and row-sparse but in different proportions, which are more similar in successive component matrices.”
A Survey on Multi-Task Learning (2017, methodology): “Different from the aforementioned methods which have only two component matrices, an arbitrary number of component matrices are considered in [105] with”
Multi-Task Learning and Algorithmic Stability (2015, methodology): “…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 decomposes the model parameters into two or more parts with each part modeling one type of sparsity, and constructing…”
Learning Multi-Level Task Groups in Multi-Task Learning (2015, methodology): “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).”
Learning Tree Structure in Multi-Task Learning (2015, methodology): “We compare the TAT method with several state-of-the-art MTL models, including the multi-task feature learning (MTFL) model [19],(3) the dirty model (Dirty) [15],(3) the Cascade model [28], the clustered multi-task learning (CMTL) model [14],(4) the grouping and overlap MTL (GOMTL) model [18], and th”
Learning to Multitask (2018, background): “This approach can be further classiﬁed into ﬁve categories: low-rank approach [1, 26], task clustering approach [16, 18], task relation learning approach [35, 32, 36, 19, 33], dirty approach [10, 17], and multi-level approach [37, 15].”
Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework (2014, methodology): “…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 Weinshall 2013).”
Which Looks Like Which: Exploring Inter-class Relationships in Fine-Grained Visual Categorization (2014, methodology): “Note that a similar objective function with mixture norms and the hinge loss was investigated in a recent work [46] for other purposes using a different optimization strategy.”
Generalized Dictionary for Multitask Learning with Boosting (2016, background): “Other models consider the existence of outlier tasks [Chen et al., 2011; Gong et al., 2012] or hierarchical structure of model parameters [Zweig and Weinshall, 2013].”
Discriminative Transfer Learning with Tree-based Priors (2013, background): “A large class of discriminative models [12, 25, 11] have also been used for transfer learning that enable discovering and sharing information among related classes.”
Parallel Multi-task Learning (2015, methodology): “We classify regularized multi-task algorithms into two main categories: learning with feature covariance [5], [7], [6], [8], [9], [10], [11], [19], [20], [21], [22] and learning with task relations [2], [3], [4], [13], [15], [14], [17], [18], [16].”