Convex Multi-Task Learning by Clustering

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An Overview of Multi-task Learning (2018, methodology): “Unlike[29,30],whichareBayesianmodels,there are several regularized methods [31–35] to do task clustering.”
A Survey on Multi-Task Learning (2017, methodology): “Barzilai and Crammer [68] propose a task clustering method by defining W as W = FG where F ∈ Rd×r and G ∈ {0, 1}r×m.”
Subspace Regularizers for Few-Shot Class Incremental Learning (2021, background): “Also related is the work of Barzilai & Crammer (2015), which synthesizes the novel weights as linear combinations of base weights; we adopt a regularization approach that allows learning of class representations that are not strict linear combinations of base classes.”
Diverse Few-Shot Text Classification with Multiple Metrics (2018, methodology): “Previous works (Kumar and Daume III, 2012; Kang et al., 2011; Cram-mer and Mansour, 2012; Barzilai and Crammer, 2015) mainly focused on convex objectives, and assumed the number of classes is the same across different tasks ( e.g. binary classiﬁcation is often considered).”
Multi-task Learning with Labeled and Unlabeled Tasks (2016, background): “Follow-up works extended and generalized these concepts, e.g. by learning the relatedness of tasks [36, 19] or sharing only between subgroups of tasks [40, 22, 6].”
Multi-Task Multi-View Clustering (2016, background): “For example, the tasks for clustering the web pages from four universities are four related tasks.”