Publications related to The analysis of kernel ridge regression learning algorithm.

Automatic L2 Regularization for Multiple Generalized Additive Models

Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expan ...

EPFL2019

Hybrid-Spline Dictionaries for Continuous-Domain Inverse Problems

Michaël Unser, Shayan Aziznejad, Thomas Jean Debarre

We study one-dimensional continuous-domain inverse problems with multiple generalized total-variation regularization, which involves the joint use of several regularization operators. Our starting point is a new representer theorem that states that such in ...

2019

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Gabriel Girard, David Paul Roger Romascano, Marco Pizzolato, Jonathan Rafael Patino Lopez, Alessandro Daducci, Muhamed Barakovic, Gaëtan Olivier D Rensonnet

Spherical deconvolution methods are widely used to estimate the brain's white-matter fiber orientations from diffusion MRI data. In this study, eight spherical deconvolution algorithms were implemented and evaluated. These included two model selection tech ...

2019

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

Markus Schneider

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

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Martin Jaggi, Chenxin Ma

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

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We investigate a class of spectral/regularized algorithms, including ridge regressi ...

2018

Comparing molecules and solids across structural and alchemical space

Michele Ceriotti, Sandip De

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is cr ...

Royal Society of Chemistry2016

Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

Ali H. Sayed

In many fields, and especially in the medical and social sciences and in recommender systems, data are gathered through clinical studies or targeted surveys. Participants are generally reluctant to respond to all questions in a survey or they may lack info ...

IEEE2016

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

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

The analysis of kernel ridge regression learning algorithm.

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Automatic L2 Regularization for Multiple Generalized Additive Models

Hybrid-Spline Dictionaries for Continuous-Domain Inverse Problems

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

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

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Comparing molecules and solids across structural and alchemical space

Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Automatic L2 Regularization for Multiple Generalized Additive Models

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Comparing molecules and solids across structural and alchemical space

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

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

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

Hybrid-Spline Dictionaries for Continuous-Domain Inverse Problems