Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression

By Piyush Rai, Abhishek Kumar, Hal Daumé Iii. 2012

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Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “The MTRL model is extended in [91] to use the symmetric matrixvariate generalized hyperbolic distribution to learn block sparse structure in W and in [92] to use the matrix generalized inverse Gaussian prior to learn low-rank Ω1 and Ω2.”
Attribute relation learning for zero-shot classification (2014, methodology): “More recently, this approach has been widely applied to many domains demonstrating its effectiveness, such as multi-task learning [44–48], transfer learning [49], multilabel learning [50,51], and multi-view learning [52].”
Learning Sparse Task Relations in Multi-Task Learning (2017, methodology): “…trace of Ω but also learns sparse task relations via the penalization of the off-diagonal entries in Ω. Note that problem (3) is different from the methods proposed in (Zhang and Schneider 2010; Rai, Kumar, and Daume 2012) whose g(·) takes the form of g(Ω) = ‖Ω−1‖1 by assuming that Ω−1 is sparse.”
Multi-task Sparse Structure Learning with Gaussian Copula Models (2016, methodology): “Xue et al. (2007) employed a Dirichlet process prior over the task coefficients to encourage task clustering and the number of clusters was someway automatically determined by the prior.”
Learning to Multitask (2018, methodology): “The survey [34] has shown that the models proposed in [14, 13, 16, 32, 27, 33] can be formulated as problem (1) with different g ( · ) ’s, where the detailed connections between these works and problem (1) are put in the supplementary material for completeness.”