Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear
  Multivariate Regression and Granger Causality   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.

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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ