Publications related to A Unifying Representer Theorem for Inverse Problems and Machine Learning

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme a ...

MICROTOME PUBL2018

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

Michaël Unser, Julien René Pierre Fageot, Anaïs Laure Marie-Thérèse Badoual

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical ...

2018

Robust and adaptive approaches for Relative Geologic Time Estimation

Mireille El Gheche

For a geoscientist, the Relative Geologic Time (RGT) is an important tool to perform chronostratigraphic analysis. However, automatically estimate an RGT image from a seismic image can be a challenging task where we have to respect seismic features, the de ...

ELSEVIER SCIENCE BV2018

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

Xin Liu, Hanjie Pan, Feng Xue, Jiaqi Liu

Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-dr ...

Ieee2017

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017

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

Dominique Bonvin, René Uwe Schneider, Predrag Milosavljevic

The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...

2016

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

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

Morphological component analysis for sparse regularization in plane wave imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Eric Bezzam, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson

Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-p ...

Ieee2016

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

Volkan Cevher

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease stati ...

Ieee-Inst Electrical Electronics Engineers Inc2015

A Unifying Representer Theorem for Inverse Problems and Machine Learning

Graph Chatbot

Chat with Graph Search

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

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

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Robust and adaptive approaches for Relative Geologic Time Estimation

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

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

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

Acoustic DoA Estimation by One Unsophisticated Sensor

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

Morphological component analysis for sparse regularization in plane wave imaging