Publications related to Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Noncontacting benchtop measurements of the elastic properties of shales

We evaluated a laser-based noncontacting method to measure the elastic anisotropy of horizontal shale cores. Whereas conventional transducer data contained an ambiguity between phase and group velocity measurements, small laser source and receiver footprin ...

2013

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

Towards Predicting the Runtime of Iterative Analytics with PREDIcT

Anastasia Ailamaki, Adrian Daniel Popescu

Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...

EPFL2013

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Anastasia Ailamaki, Adrian Daniel Popescu

Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...

2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Volkan Cevher, Anastasios Kyrillidis

The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...

IEEE2013

Randomized Extended Kaczmarz For Solving Least Squares

Nikolaos Freris

We present a randomized iterative algorithm that exponentially converges in the mean square to the minimum l(2)-norm least squares solution of a given linear system of equations. The expected number of arithmetic operations required to obtain an estimate o ...

Society for Industrial and Applied Mathematics2013

Accelerated And Inexact Forward-Backward Algorithms

Luca Baldassarre

We propose a convergence analysis of accelerated forward-backward splitting methods for composite function minimization, when the proximity operator is not available in closed form, and can only be computed up to a certain precision. We prove that the 1/k( ...

Siam Publications2013

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We introduce a new wavelet-based method for the implementation of Total-Variation-type denoising. The data term is least-squares, while the regularization term is gradient-based. The particularity of our method is to exploit a link between the discrete gra ...

IEEE2012

Exploiting Local Quasiconvexity for Gradient Estimation in Modifier-Adaptation Schemes

Dominique Bonvin, Grégory François, Gene Bunin

A new approach for gradient estimation in the context of real-time optimization under uncertainty is proposed in this paper. While this estimation problem is often a difficult one, it is shown that it can be simplified significantly if an assumption on the ...

Ieee Computer Soc2012

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Graph Chatbot

Chat with Graph Search

Noncontacting benchtop measurements of the elastic properties of shales

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Towards Predicting the Runtime of Iterative Analytics with PREDIcT

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Randomized Extended Kaczmarz For Solving Least Squares

Accelerated And Inexact Forward-Backward Algorithms

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Exploiting Local Quasiconvexity for Gradient Estimation in Modifier-Adaptation Schemes

Accelerated And Inexact Forward-Backward Algorithms

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Towards Predicting the Runtime of Iterative Analytics with PREDIcT

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Randomized Extended Kaczmarz For Solving Least Squares

Noncontacting benchtop measurements of the elastic properties of shales

Exploiting Local Quasiconvexity for Gradient Estimation in Modifier-Adaptation Schemes