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• W1823924931 abstract "We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. We develop a highly scalable and eigendecomposition-free algorithm that orchestrates two inexact solvers for simultaneously learning both the input and output components of separable matrix-valued kernels. As a key application enabled by our framework, we show how high-dimensional causal inference tasks can be naturally cast as sparse function estimation problems, leading to novel nonlinear extensions of a class of Graphical Granger Causality techniques. Our algorithmic developments and extensive empirical studies are complemented by theoretical analyses in terms of Rademacher generalization bounds." @default.
• W1823924931 created "2016-06-24" @default.
• W1823924931 creator A5007571091 @default.
• W1823924931 creator A5055054658 @default.
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• W1823924931 date "2012-10-17" @default.
• W1823924931 modified "2023-09-27" @default.
• W1823924931 title "Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality" @default.
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• W1823924931 hasPublicationYear "2012" @default.
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