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A New Global Stochastic Search Approach for Inverse Problems: Application to Ultrasound Modulated Optical Tomography
inverse problems global stochastic search discretized Kushner-Stratonovich equation gain-based update ultrasound modulated optical tomography
2013/6/14
A global stochastic search method, which is strictly derivative-free yet directed through a gain-based additive update term, is proposed and applied to the inverse problem of ultrasound modulated opti...
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
2013/6/14
Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the mini-batc...
Moment based estimation of supOU processes and a related stochastic volatility model
generalized method of moments Ornstein-Uhlenbeck type process L
2013/6/14
After a quick review of superpositions of OU (supOU) processes, integrated supOU processes and the supOU SV model we estimate these processes by using the generalized method of moments. We show that t...
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
Adaptivity averaged stochastic gradient descent local strong convexity logistic regression
2013/4/28
In this paper, we consider supervised learning problems such as logistic regression and study the stochastic gradient method with averaging, in the usual stochastic approximation setting where observa...
Performance of the stochastic MV-PURE estimator in highly noisy settings
robust linear estimation reduced-rank estimation stochastic MV-PURE estimator array signal processing
2013/4/28
The stochastic MV-PURE estimator has been developed to provide linear estimation robust to ill-conditioning, high noise levels, and imperfections in model knowledge. In this paper, we investigate the ...
Statistical inference for discrete-time samples from affine stochastic delay differential equations
asymptotic normality composite likelihood consistency discrete time observation of continuous-time models prediction-based estimating functions pseudo-likelihood stochastic delay differential equation
2013/4/28
Statistical inference for discrete time observations of an affine stochastic delay differential equation is considered. The main focus is on maximum pseudo-likelihood estimators, which are easy to cal...
Model selection and clustering in stochastic block models with the exact integrated complete data likelihood
Random graphs stochastic block models integrated classication likelihood
2013/4/27
The stochastic block model (SBM) is a mixture model used for the clustering of nodes in networks. It has now been employed for more than a decade to analyze very different types of networks in many sc...
Classification of Segments in PolSAR Imagery by Minimum Stochastic Distances Between Wishart Distributions
Region-Based Classification Stochastic Distances Hypothesis Tests Polarimetry Wishart distribution
2013/4/28
A new classifier for Polarimetric SAR (PolSAR) images is proposed and assessed in this paper. Its input consists of segments, and each one is assigned the class which minimizes a stochastic distance. ...
The linear stochastic order and directed inference for multivariate ordered distributions
Nonparametric tests order-restricted statistical inference stochastic order relations
2013/4/27
Researchers are often interested in drawing inferences regarding the order between two experimental groups on the basis of multivariate response data. Since standard multivariate methods are designed ...
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization
Bandit Derivative-Free Stochastic Convex Optimization
2012/11/23
The problem of stochastic convex optimization with bandit feedback (in the learning community) or without knowledge of gradients (in the optimization community) has received much attention in recent y...
Spectral Risk Measures, With Adaptions For Stochastic Optimization
Spectral Risk Measures Adaptions Stochastic Optimization
2012/11/22
Stochastic optimization problems often involve the expectation in its objective. When risk is incorporated in the problem description as well, then risk measures have to be involved in addition to qua...
Bayesian Adaptive Smoothing Spline using Stochastic Differential Equations
Adaptive smoothing Markov chain Monte Carlo Smoothing spline Stochastic dierential equation
2012/11/22
The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. How...
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Dual Coordinate Ascent Methods Regularized Loss Minimization
2012/11/22
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closel...
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Dual Coordinate Ascent Methods Regularized Loss Minimization
2012/11/22
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closel...
A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models
Population Monte Carlo importance sampling degeneracy of importance weights stochastic kinetic models
2012/9/18
This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (P...