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An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
logistic regression feature selection regularization regularization path
2015/7/10
Logistic regression with l1 regularization has been proposed as a promising method for feature selection in classification problems. In this paper we describe an efficient interior-point method for so...
An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems
Signal processing algorithms stochastic parameters parameter estimation
2015/7/9
We present an alternating augmented Lagrangian method for convex optimization problems where the cost function is the sum of two terms, one that is separable in the variable blocks, and a second that ...
SOLVING REGULARIZED LINEAR PROGRAMS USING BARRIER METHODS AND KKT SYSTEMS
barrier methods interior methods linear programming
2015/7/3
We discuss the solution of regularized linear programs using a primal-dual barrier
method. Our implementation is based on indeˉnite Cholesky-type factorizations of full and reduced
KKT systems. Regu...
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Regularized M-estimators nonconvexity Statistical algorithmic theory local optima
2013/6/14
We establish theoretical results concerning all local optima of various regularized M-estimators, where both loss and penalty functions are allowed to be nonconvex. Our results show that as long as th...
Practical Tikhonov Regularized Estimators in Reproducing Kernel Hilbert Spaces for Statistical Inverse Problems
Tikhonov Regularized Estimators Reproducing Kernel Hilbert Spaces Statistical Inverse Problems
2013/6/13
Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and comm...
Testing Hypotheses by Regularized Maximum Mean Discrepancy
Testing Hypotheses Regularized Maximum Mean Discrepancy
2013/6/14
Do two data samples come from different distributions? Recent studies of this fundamental problem focused on embedding probability distributions into sufficiently rich characteristic Reproducing Kerne...
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
A General Iterative Shrinkage Thresholding Algorithm Non-convex Regularized Optimization Problems
2013/5/2
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterp...
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Efficient Regularized；Least-Squares；Algorithms；Conditional Ranking；Relational Data
2012/11/23
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular ta...
Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood
High dimensionality unknown factors principal components sparse matrix conditional sparse thresholding cross-sectional correlation penalized maximum likelihood adaptive lasso heteroskedasticity
2012/11/23
We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis ...
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...
Local shrinkage rules, Lévy processes, and regularized regression
Local shrinkage rules Lévy processes regularized regression
2010/10/19
We use L\'evy processes to generate joint prior distributions for a location parameter $\bbeta = (\beta_1,...,\beta_p) $ as $p$ grows large. This leads to the class of local-global shrinkage rules. We...
High-dimensional Ising model selection using ${\ell_1}$-regularized logistic regression
High-dimensional model selection
2010/10/14
We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of...
Level crossing and local time for regularized Gaussian processes
Level crossing local time regularized Gaussian processes
2009/9/22
Let (X,,t~ [0, 11) be a oentred stationary Gaussian
process defined on (D,A , P) with covariance function satisfying
Define the regularized process
X' = cp, * X and YE = Xc/oe, where CT~ = var Xf ,...
Online data processing: Comparison of Bayesian regularized particle filters
Online data processing Bayesian estimation regularized particle filters Stochastic Volatility models
2009/9/16
The aim of this paper is to compare three regularized particle filters in an online data processing context. We carry out the comparison in terms of hidden states filtering and parameter estimation, c...