Publications related to Hessian-Based Regularization for 3-D Microscopy Image Restoration

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Metal cations often play an important role in shaping the three-dimensional structure of peptides. As an example, the model system AcPheAla5LysH+ is investigated in order to fully understand the forces that stabilize its helical structure. In particular, t ...

EPFL2018

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

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

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

Michaël Unser

Ill-posed inverse problems are often constrained by imposing a bound on the total variation of the solution. Here, we consider a generalized version of total-variation regularization that is tied to some differential operator L. We then show that the gener ...

OSA2016

Diffusion stochastic optimization with non-smooth regularizers

Ali H. Sayed, Stefan Vlaski

We develop an effective distributed strategy for seeking the Pareto solution of an aggregate cost consisting of regularized risks. The focus is on stochastic optimization problems where each risk function is expressed as the expectation of some loss functi ...

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

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Estimating the ice thickness of shallow glaciers from surface topography and mass-balance data with a shape optimization algorithm

Marco Picasso, Laurent Michel, Heinz Blatter, Martin Funk

A shape optimization algorithm is presented that estimates the ice thickness distribution within a three-dimensional, shallow glacier, given a transient surface geometry and a mass-balance distribution. The approach is based on the minimization of the surf ...

Pergamon-Elsevier Science Ltd2014

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Alireza Karimi

Noniterative data-driven techniques are design methods that allow optimal feedback control laws to be derived from input-output (I/O) data only, without the need of a model of the process. A drawback of these methods is that, in their standard formulation, ...

Pergamon-Elsevier Science Ltd2014

Stability and Hypothesis Transfer Learning

Ilja Kuzborskij

We consider the transfer learning scenario, where the learner does not have access to the source domain directly, but rather operates on the basis of hypotheses induced from it - the Hypothesis Transfer Learning (HTL) problem. Particularly, we conduct a th ...

2013

Hessian-Based Regularization for 3-D Microscopy Image Restoration

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Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Robust and adaptive approaches for Relative Geologic Time Estimation

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

Diffusion stochastic optimization with non-smooth regularizers

Morphological component analysis for sparse regularization in plane wave imaging

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Estimating the ice thickness of shallow glaciers from surface topography and mass-balance data with a shape optimization algorithm

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Stability and Hypothesis Transfer Learning

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Robust and adaptive approaches for Relative Geologic Time Estimation

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Morphological component analysis for sparse regularization in plane wave imaging

Stability and Hypothesis Transfer Learning

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Estimating the ice thickness of shallow glaciers from surface topography and mass-balance data with a shape optimization algorithm

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

Diffusion stochastic optimization with non-smooth regularizers

Enhancing statistical performance of data-driven controller tuning via L2-regularization