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Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel Hilbert Spaces.
A course in abstract harmonic analysis
G. B. Folland · 1994
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Fourier Analysis on groups
W. Rudin · 1994
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Using the fisher kernel method to detect remote protein homologies
T. Jaakkola, M. Diekhans, and D. Haussler · 1999
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Learning multiple tasks with kernel methods
T. Evgeniou, C. A. Micchelli, and M. Pontil · 2005
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On learning vector-valued functions
C. A. Micchelli and M. A. Pontil · 2005
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Refined Error Estimates for Matrix-Valued Radial Basis Functions
E. Fuselier · 2006
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Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2007
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Refinement of operator-valued reproducing kernels
H. Zhang, Y. Xu, and Q. Zhang · 2007
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Learning div-free and curl-free vector fields by matrix-valued kernels
Y. Macedo and R. Castro · 2008
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The matrix cookbook
K. B. Petersen, M. S. Pedersen, et al · 2008
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Idr (s): A family of simple and fast algorithms for solving large nonsymmetric systems of linear equations
P. Sonneveld and M. B. van Gijzen · 2008
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Vector field learning via spectral filtering
L. Baldassarre, L. Rosasco, A. Barla, and A. Verri · 2010
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Vector valued reproducing kernel hilbert spaces and universality
C. Carmeli, E. De Vito, A. Toigo, and V. Umanità · 2010
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Learning output kernels with block coordinate descent
F. Dinuzzo, C. Ong, P. Gehler, and G. Pillonetto · 2011
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Lsmr: An iterative algorithm for sparse least-squares problems
D. C.-L. Fong and M. Saunders · 2011
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Kernels for vector-valued functions: a review
M. A. Álvarez, L. Rosasco, and N. D. Lawrence · 2012
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Multi-output learning via spectral filtering
L. Baldassarre, L. Rosasco, A. Barla, and A. Verri · 2012
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Fastfood - computing hilbert space expansions in loglinear time
Q. V. Le, T. Sarlós, and A. J. Smola · 2013
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Matrix-valued kernels for shape deformation analysis
M. Micheli and J. Glaunes · 2013
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Scalable matrix-valued kernel learning for high-dimensional nonlinear multivariate regression and granger causality
V. Sindhwani, H. Q. Minh, and A. Lozano · 2013
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Modeling magnetic fields using gaussian processes
N. Wahlström, M. Kok, T. Schön, and F. Gustafsson · 2013
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Matrix concentration inequalities via the method of exchangeable pairs
L. Mackey, M. I. Jordan, R. Chen, B. Farrel, and J. Tropp · 2014
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Multiclass learning with simplex coding
Y. Mroueh, T. Poggio, L. Rosasco, and J.-j. Slotine · 2012
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User-friendly tail bounds for sums of random matrices
J.-A. Tropp · 2012
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Nyström method vs random fourier features: A theoretical and empirical comparison
T. Yang, Y.-F. Li, M. Mahdavi, R. Jin, and Z. Zhou · 2012
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Random features for kernel deep convex network
P.-S. Huang, L. Deng, M. Hasegawa-Johnson, and X. He · 2013
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A remark on low rank matrix recovery and noncommutative bernstein type inequalities
V. Koltchinskii et al · 2013
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Strong converse for identification via quantum channels
R. Ahlswede and A. Winter
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Z. Yang, A. J. Smola, L. Song, and A. G. Wilson · 2014
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On the equivalence between quadrature rules and random features
F. Bach · 2015
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Operator-valued kernel-based vector autoregressive models for network inference
N. Lim, F. d’Alché-Buc, C. Auliac, and G. Michailidis · 2015
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Optimal rates for random fourier features
B. Sriperumbudur and Z. Szabo · 2015
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On the error of random fourier features
D. J. Sutherland and J. G. Schneider · 2015
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A la carte - learning fast kernels
Z. Yang, A. G. Wilson, A. J. Smola, and L. Song · 2015
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