FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2964187711> ?p ?o ?g. }

• W2964187711 endingPage "595" @default.
• W2964187711 startingPage "586" @default.
• W2964187711 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.
• W2964187711 created "2019-07-30" @default.
• W2964187711 creator A5007571091 @default.
• W2964187711 creator A5055054658 @default.
• W2964187711 creator A5058073838 @default.
• W2964187711 date "2013-08-11" @default.
• W2964187711 modified "2023-09-27" @default.
• W2964187711 title "Scalable matrix-valued kernel learning for high-dimensional nonlinear multivariate regression and Granger Causality" @default.
• W2964187711 cites W1176919 @default.
• W2964187711 cites W145994340 @default.
• W2964187711 cites W1515020792 @default.
• W2964187711 cites W1518544712 @default.
• W2964187711 cites W1540529426 @default.
• W2964187711 cites W1541700983 @default.
• W2964187711 cites W1563853427 @default.
• W2964187711 cites W1574851760 @default.
• W2964187711 cites W1775587472 @default.
• W2964187711 cites W1828831131 @default.
• W2964187711 cites W1903038885 @default.
• W2964187711 cites W1986280275 @default.
• W2964187711 cites W1992774275 @default.
• W2964187711 cites W2009271606 @default.
• W2964187711 cites W2081531193 @default.
• W2964187711 cites W2093545205 @default.
• W2964187711 cites W2099693464 @default.
• W2964187711 cites W2101593431 @default.
• W2964187711 cites W2105581244 @default.
• W2964187711 cites W2109706083 @default.
• W2964187711 cites W2115003579 @default.
• W2964187711 cites W2115021853 @default.
• W2964187711 cites W2116343548 @default.
• W2964187711 cites W2118418963 @default.
• W2964187711 cites W2118504964 @default.
• W2964187711 cites W2121033924 @default.
• W2964187711 cites W2124372976 @default.
• W2964187711 cites W2128610923 @default.
• W2964187711 cites W2144902422 @default.
• W2964187711 cites W2146611938 @default.
• W2964187711 cites W2153675946 @default.
• W2964187711 cites W2170912114 @default.
• W2964187711 cites W2171188027 @default.
• W2964187711 cites W2538008885 @default.
• W2964187711 cites W2579923771 @default.
• W2964187711 cites W2953193873 @default.
• W2964187711 cites W3021971632 @default.
• W2964187711 cites W340056678 @default.
• W2964187711 cites W45695613 @default.
• W2964187711 hasPublicationYear "2013" @default.
• W2964187711 type Work @default.
• W2964187711 sameAs 2964187711 @default.
• W2964187711 citedByCount "26" @default.
• W2964187711 countsByYear W29641877112013 @default.
• W2964187711 countsByYear W29641877112014 @default.
• W2964187711 countsByYear W29641877112015 @default.
• W2964187711 countsByYear W29641877112016 @default.
• W2964187711 countsByYear W29641877112017 @default.
• W2964187711 countsByYear W29641877112018 @default.
• W2964187711 countsByYear W29641877112019 @default.
• W2964187711 countsByYear W29641877112020 @default.
• W2964187711 countsByYear W29641877112021 @default.
• W2964187711 crossrefType "proceedings-article" @default.
• W2964187711 hasAuthorship W2964187711A5007571091 @default.
• W2964187711 hasAuthorship W2964187711A5055054658 @default.
• W2964187711 hasAuthorship W2964187711A5058073838 @default.
• W2964187711 hasConcept C106487976 @default.
• W2964187711 hasConcept C11413529 @default.
• W2964187711 hasConcept C118615104 @default.
• W2964187711 hasConcept C119857082 @default.
• W2964187711 hasConcept C121332964 @default.
• W2964187711 hasConcept C122280245 @default.
• W2964187711 hasConcept C12267149 @default.
• W2964187711 hasConcept C126255220 @default.
• W2964187711 hasConcept C129824826 @default.
• W2964187711 hasConcept C134306372 @default.
• W2964187711 hasConcept C154945302 @default.
• W2964187711 hasConcept C158622935 @default.
• W2964187711 hasConcept C158693339 @default.
• W2964187711 hasConcept C159985019 @default.
• W2964187711 hasConcept C161584116 @default.
• W2964187711 hasConcept C169756996 @default.
• W2964187711 hasConcept C177148314 @default.
• W2964187711 hasConcept C192562407 @default.
• W2964187711 hasConcept C2776214188 @default.
• W2964187711 hasConcept C28826006 @default.
• W2964187711 hasConcept C33923547 @default.
• W2964187711 hasConcept C41008148 @default.
• W2964187711 hasConcept C62520636 @default.
• W2964187711 hasConcept C62799726 @default.
• W2964187711 hasConcept C74193536 @default.
• W2964187711 hasConcept C80884492 @default.
• W2964187711 hasConceptScore W2964187711C106487976 @default.
• W2964187711 hasConceptScore W2964187711C11413529 @default.
• W2964187711 hasConceptScore W2964187711C118615104 @default.
• W2964187711 hasConceptScore W2964187711C119857082 @default.
• W2964187711 hasConceptScore W2964187711C121332964 @default.
• W2964187711 hasConceptScore W2964187711C122280245 @default.
• W2964187711 hasConceptScore W2964187711C12267149 @default.
• W2964187711 hasConceptScore W2964187711C126255220 @default.

• next