A Spectral Regularization Framework for Multi-Task Structure Learning

By Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying. 2007

253 citations recorded by the source corpus.

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Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “• Computer Survey dataset [13]: This dataset is taken from a survey of 180 persons/tasks who rated the likelihood of purchasing one of 20 different personal computers, resulting in 36,000 data points in all the tasks.”
Subspace Regularizers for Few-Shot Class Incremental Learning (2021, background): “Past work on other multitask learning problems has demonstrated the effectiveness of constraining parameters for related tasks to be similar (Jacob et al., 2008), lie on the same manifold (Agarwal et al., 2010) or even on the same linear subspace (Argyriou et al., 2007a).”
A Convex Formulation for Learning Task Relationships in Multi-Task Learning (2010, background): “For example, when q(W) = MN d×m(W∣0d×m,Σ ⊗ Im), it leads to a formulation similar to multi-task feature learning [1, 2]:”
Causal Transfer in Machine Learning (2015, methodology): “An alternative family of methods is based on learning a set of common features for all tasks [Argyriou et al., 2007a, Romera-Paredes et al., 2012, Argyriou et al., 2007b, Raina et al., 2007].”
Deep Multi-task Representation Learning: A Tensor Factorisation Approach (2016, methodology): “For example, School dataset [Argyriou et al., 2007] is MDL as the target is to predict students’ exam score (one single-output task) where students are grouped by school (multiple domains).”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, unknown): “the model parameter space [223], [229], [230].”
Recursive regularization for large-scale classification with hierarchical and graphical dependencies (2013, methodology): “Indirectly related to our paper are a few works in multitask learning [12], [2],[3], [32] where regularization was used as a tool to share information between tasks.”
Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model (2013, methodology): “The regularization method is widely used in multi-task learning to discover the relationship among the tasks (Evgeniou & Pontil, 2004; Argyriou et al., 2007; Agarwal et al., 2010; Jenatton et al., 2011).”
Multi-Task Averaging (2011, unknown): “A common approach is to estimate the similarity matrix jointly with the task parameters [12, 13, 5, 14, 15].”
Clustered Multi-Task Learning Via Alternating Structure Optimization (2011, background): “T task relatedness can also be modeled by constraining multiple tasks to share a common underl ying structure [4, 6, 9, 10].”
Learning with Whom to Share in Multi-task Feature Learning (2011, methodology): “In particular, we contrast our work to a recent technique proposed in (Argyriou et al., 2008b), where the problem of inferring task clusters is cast as learning a set of kernel functions, one for each group.”
Revisiting Stein's paradox: multi-task averaging (2014, unknown): “In this paper we do just that: apply this regularizer to mean estimation, show that this special case enables new and useful analytic results, and demonstrate its performance with simulated and real data.”
Multitask learning meets tensor factorization: task imputation via convex optimization (2014, methodology): “3, we discuss experiments on two real world data sets, namely the Restaurant data set [26] and School Effectiveness data set [3, 4].”
Learning to Multitask (2018, background): “As revealed in the survey [34], there is another representative formulation for the feature-based approach [2, 3, 11] in multitask learning.”