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When Is There a Representer Theorem? Vector Versus Matrix Regularizers

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The Rademacher Complexity of Linear Transformation Classes

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A Generalized Representer Theorem

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The Nature of Statistical Learning Theory

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A Function Representation for Learning in Banach Spaces

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An Algorithm for Transfer Learning in a Heterogeneous Environment

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Lectures on optimal recovery

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Exact Matrix Completion via Convex Optimization

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Convex multi-task feature learning

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Reduced-rank regression for the multivariate linear model

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Kernel Methods for Pattern Analysis

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Topics in Matrix Analysis

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Regularization Networks and Support Vector Machines

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Theory of reproducing kernels

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On the mathematical foundations of learning

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A rank minimization heuristic with application to minimum order system approximation

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Model Selection and Estimation in Regression with Grouped Variables

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Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression

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Probabilistic Joint Feature Selection for Multi-task Learning

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Semidefinite Programming

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Constructing informative priors using transfer learning

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Uncovering shared structures in multiclass classification

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A training algorithm for optimal margin classifiers

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On Learning Vector-Valued Functions

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Regularization Theory and Neural Networks Architectures

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Regularization of Wavelet Approximations

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Component selection and smoothing in multivariate nonparametric regression

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A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines

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Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs

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Solutions of Ill-Posed Problems.

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Spline Models for Observational Data.

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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

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On the Representer Theorem and Equivalent Degrees of Freedom of SVR

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Multi-Task Feature Learning

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