Publications associées à Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Gabor dual windows using convex optimization

Redundant Gabor frames admit an infinite number of dual frames, yet only the canonical dual Gabor system, con- structed from the minimal l2-norm dual window, is widely used. This window function however, might lack desirable properties, such as good time-f ...

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

Interchangeability with thresholds and degradation factors for Soft CSPs

Boi Faltings, Nicoleta Neagu

Substitutability and interchangeability in constraint satisfaction problems (CSPs) have been used as a basis for search heuristics, solution adaptation and abstraction techniques. In this paper, we consider how the same concepts can be extended to soft con ...

Springer2013

Distributionally Robust Joint Chance Constraints with Second-Order Moment Information

Daniel Kuhn

We develop tractable semidefinite programming based approximations for distributionally robust individual and joint chance constraints, assuming that only the first- and second-order moments as well as the support of the uncertain parameters are given. It ...

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

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

Hessian Schatten-Norm Regularization for Linear Inverse Problems

Michaël Unser, John Paul Ward, Stamatios Lefkimmiatis

We introduce a novel family of invariant, convex, and non-quadratic functionals that we employ to derive regularized solutions of ill-posed linear inverse imaging problems. The proposed regularizers involve the Schatten norms of the Hessian matrix, which a ...

Ieee-Inst Electrical Electronics Engineers Inc2013

An exact algorithm for the integrated planning of berth allocation and quay crane assignment

Michel Bierlaire, Matteo Salani, Ilaria Vacca

In this paper we study the simultaneous optimization of berth allocation and quay crane assignment in seaport container terminals. We propose a model based on an exponential number of variables that is solved via column generation. An exact branch and pric ...

Informs2013

To Convexify or Not? Regression with Clustering Penalties on Graphs

Volkan Cevher, Luca Baldassarre, Marwa El Halabi

We consider minimization problems that are compositions of convex functions of a vector

\x \in\reals^N

with submodular set functions of its support (i.e., indices of the non-zero coefficients of

\x

). Such problems are in general difficult for large

N

...

IEEE2013

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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Gabor dual windows using convex optimization

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

Interchangeability with thresholds and degradation factors for Soft CSPs

Distributionally Robust Joint Chance Constraints with Second-Order Moment Information

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

Hessian Schatten-Norm Regularization for Linear Inverse Problems

An exact algorithm for the integrated planning of berth allocation and quay crane assignment

To Convexify or Not? Regression with Clustering Penalties on Graphs

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Distributionally Robust Joint Chance Constraints with Second-Order Moment Information

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

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

An exact algorithm for the integrated planning of berth allocation and quay crane assignment

Gabor dual windows using convex optimization

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

Interchangeability with thresholds and degradation factors for Soft CSPs

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

Hessian Schatten-Norm Regularization for Linear Inverse Problems

To Convexify or Not? Regression with Clustering Penalties on Graphs