Estimating variable structure and dependence in multitask learning via gradients

DOI10.1007/s10994-010-5217-4zbMath1237.68145OpenAlexW2101633703WikidataQ56906096 ScholiaQ56906096MaRDI QIDQ415606

Qiang Wu, Justin Guinney, Sayan Mukherjee

Publication date: 8 May 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-010-5217-4

zbMATH Keywords

dimension reductiongraphical modelscovariance estimationinverse regressionmultitask learning

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20) Learning and adaptive systems in artificial intelligence (68T05) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (1)

Least square regularized regression for multitask learning

Cites Work

• Fisher lecture: Dimension reduction in regression
• Learning gradients on manifolds
• Convex multi-task feature learning
• Gaussian Markov distributions over finite graphs
• Sliced Inverse Regression for Dimension Reduction
• An Adaptive Estimation of Dimension Reduction Space
• Learning Theory and Kernel Machines
• Comment
• The elements of statistical learning. Data mining, inference, and prediction
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