Publications associées à High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

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Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Michaël Unser, Julien René Pierre Fageot, Harshit Gupta

We present a theoretical analysis and comparison of the effect of

ℓ _{ 1 }

versus

ℓ _{ 2 }

regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the s ...

IEEE2016

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

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Spatially Varying Monte Carlo SURE for the Regularization of Biomedical Images

Regularization via Mass Transportation

Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

An Equivalence between the Lasso and Support Vector Machines

Spatially Varying Monte Carlo SURE for the Regularization of Biomedical Images

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Regularization via Mass Transportation

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

An Equivalence between the Lasso and Support Vector Machines

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Sparsity-Based Data Reconstruction Models for Biomedical Imaging