Publications associées à A Unifying Representer Theorem for Inverse Problems and Machine Learning

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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

A Unifying Representer Theorem for Inverse Problems and Machine Learning

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CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Robust and adaptive approaches for Relative Geologic Time Estimation

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

Acoustic DoA Estimation by One Unsophisticated Sensor

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Morphological component analysis for sparse regularization in plane wave imaging

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Robust and adaptive approaches for Relative Geologic Time Estimation

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

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Acoustic DoA Estimation by One Unsophisticated Sensor

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

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

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Morphological component analysis for sparse regularization in plane wave imaging