Publications related to Stochastic gradient descent

Efficient Sample Mining for Object Detection

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have devel ...

2014

Sample Distillation for Object Detection and Image Classification

François Fleuret, Olivier Canévet, Leonidas Lefakis

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have devel ...

2014

Scalable sparse covariance estimation via self-concordance

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis, Rabeeh Karimi Mahabadi

We consider the class of convex minimization problems, composed of a self-concordant function, such as the logdet metric, a convex data fidelity term h and, a regularizing -- possibly non-smooth -- function g. This type of problems have recently attracted ...

2014

Predictive Control for Longitudinal Beam Dynamics in Heavy Ion Synchrotrons

Timm Faulwasser

We present a design of a predictive control scheme for longitudinal beam dynamics in heavy ion synchrotrons. Specifically, we consider a linear-quadratic model predictive control (MPC) approach, whereby the quadratic program is solved via a fast gradient m ...

2014

Convex Optimization for Big Data

Volkan Cevher

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniqu ...

Institute of Electrical and Electronics Engineers2014

Monte-Carlo utility estimates for Bayesian reinforcement learning

Christos Dimitrakakis

This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct an optimistic policy. Secondly, gradient-based algorithm ...

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

Analysis of Descent-Based Image Registration

Pascal Frossard, Elif Vural

We present a performance analysis for image registration with gradient descent methods. We consider a typical multiscale registration setting where the global 2-D translation between a pair of images is estimated by smoothing the images and minimizing the ...

2013

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

Stochastic gradient descent

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Efficient Sample Mining for Object Detection

Sample Distillation for Object Detection and Image Classification

Scalable sparse covariance estimation via self-concordance

Predictive Control for Longitudinal Beam Dynamics in Heavy Ion Synchrotrons

Convex Optimization for Big Data

Monte-Carlo utility estimates for Bayesian reinforcement learning

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

Analysis of Descent-Based Image Registration

Accelerated And Inexact Forward-Backward Algorithms

Convex Optimization for Big Data

Efficient Sample Mining for Object Detection

Analysis of Descent-Based Image Registration

Predictive Control for Longitudinal Beam Dynamics in Heavy Ion Synchrotrons

Sample Distillation for Object Detection and Image Classification

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

Scalable sparse covariance estimation via self-concordance

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

Monte-Carlo utility estimates for Bayesian reinforcement learning

Accelerated And Inexact Forward-Backward Algorithms