Union Support Recovery in Multi-task Learning

By Mladen Kolar, John D. Lafferty, Larry A. Wasserman. 2010

60 citations recorded by the source corpus.

We sharply characterize the performance of different penalization schemes for the problem of selecting the relevant variables in the multi-task setting. Previous work focuses on the regression problem where conditions on the design matrix complicate the analysis. A clearer and simpler picture emerges by studying the Normal means model. This model, often used in the field of statistics, is a simplified model that provides a laboratory for studying complex procedures.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

A Survey on Multi-Task Learning (2017, background): “Several studies [230], [231], [232] investigate conditions to well recover true features for multi-task feature selection methods.”
Multi-Stage Multi-Task Feature Learning (2012, neutral): “…et al., 2010; Negahban and Wainwright, 2011) and a common set of shared features (Argyriou et al., 2008; Gong et al., 2012; Kim and Xing, 2009; Kolar et al., 2011; Lounici et al., 2009; Liu et al., 2009; Negahban and Wainwright, 2008; Obozinski et al., 2006; Yang et al., 2009; Zhang et al.,…”
Multi-Task Learning and Algorithmic Stability (2015, neutral): “Kolar et al. (2011) investigate to recover sparse patterns.”
Group Lasso with Overlaps: the Latent Group Lasso approach (2011, background): “The work of several authors shows that when the support can be encoded well by the groups defining the norm, support recovery and estimation are improved (Huang and Zhang, 2010; Kolar et al., 2011; Lounici et al., 2010, 2009; Negahban and Wainwright, 2011; Obozinski et al., 2010).”
Efficient multi-task feature learning with calibration (2014, background): “Most existing multi-task feature learning models can be formulated as a regularized optimization problem and they usually focus on how to design a good regularizer to capture the underlying shared features among tasks; examples include group lasso multi-task feature learning [1, 19, 23, 25, 29, 33, ”
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations (2013, unknown): “These vectors are by no means the only interesting estimands constructed from μt and μt,t′ ; many others are discussed by Lechner (2001), Heckman and Vytlacil (2007), and others.”