Publications related to An Efficient Mutual Information Optimizer for Multiresolution Image Registration

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space. We prove convergence results with respect to variants of n ...

ICML2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

Alexandre Caboussat, Dimitrios Gourzoulidis

In this article, we address the numerical solution of the Dirichlet problem for the three-dimensional elliptic Monge-Ampere equation using a least-squares/relaxation approach. The relaxation algorithm allows the decoupling of the differential operators fro ...

SPRINGER/PLENUM PUBLISHERS2018

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

Volkan Cevher, Quoc Tran Dinh

We propose two new alternating direction methods to solve “fully” nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarantee under mild assumptions for both the objective residual and feasibility gap ...

Springer2018

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Nicolas Henri Bernard Flammarion, Aymeric Daphnis Kévin Dieuleveut

We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...

2017

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

Alberto Crivellaro

We propose new algorithms to overcome two of the most constraining limitations of surface reconstruction methods in use. In particular, we focus on the large amount of data characterizing standard acquisitions by scanner and the noise intrinsically introdu ...

Elsevier Science Bv2017

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

Fabio Nobile, Giovanni Migliorati

Motivated by the numerical treatment of parametric and stochastic PDEs, we analyze the least-squares method for polynomial approximation of multivariate functions based on random sampling according to a given probability measure. Recent work has shown that ...

2015

From averaging to acceleration, there is only a step-size

Nicolas Henri Bernard Flammarion

We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

Idiap2015

An Efficient Mutual Information Optimizer for Multiresolution Image Registration

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Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Mean-Field methods for Structured Deep-Learning in Computer Vision

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

From averaging to acceleration, there is only a step-size

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Mean-Field methods for Structured Deep-Learning in Computer Vision

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

From averaging to acceleration, there is only a step-size

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations