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Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
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2015/8/21
We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be non...
Dynamic Emission Tomography— Regularization and Inversion
Dynamic Emission Tomography Regularization Inversion
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2015/7/10
The problem of emission tomography, inverting the attenuated Radon transform, is moderately ill-posed if the unknown emission source is static. Here we consider the case where the emission source is d...
Segmentation of ARX-Models Using Sum-of-Norms Regularization
Segmentation Regularization ARX-models
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2015/7/9
Segmentation of time-varying systems and signals into models whose parameters are piecewise constant in time is an important and well studied problem. It is here formulated as a least-squares problem ...
Smoothed state estimates under abrupt changes using sum-of-norms regularization
Smoothing Sparsity Regularization Change detection
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2015/7/9
The presence of abrupt changes, such as impulsive and load disturbances, commonly occur in applications, but make the state estimation problem considerably more difficult than in the standard setting ...
Cholesky-based Methods for Sparse Least Squares: The Beneˉts of Regularization
Sparse Least Squares Regularization
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2015/7/3
We study the use of black-box LDL
T
factorizations for solving the augmented
systems (KKT systems) associated with least-squares problems and barrier methods
for linear programming (LP). With judi...
Efficiently Using Second Order Information in Large l1 Regularization Problems
Efficiently Using Second Order Information Large l1 Regularization Problems
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2013/4/28
We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems. Our method executes cheap iterations while achieving f...
Convex Tensor Decomposition via Structured Schatten Norm Regularization
Convex Tensor Decomposition Structured Schatten Norm Regularization
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2013/4/28
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tens...
On Sparsity Inducing Regularization Methods for Machine Learning
Sparsity Inducing Regularization Methods for Machine Learning
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2013/5/2
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer ...
Nonparametric sparsity and regularization
Sparsity Nonparametrics Variable selection Regularization Proximal meth-ods RKHS
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2012/9/17
In this work we are interested in the problems of supervised learning and variable se-lection when the input-output dependence is described by a nonlinear function depending on a few variables. Our go...
spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
MCMC P-splines spike-and-slab prior normal-inverse-gamma
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2011/6/20
The R package spikeSlabGAM implements Bayesian variable selection, model choice,
and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and
Poisson responses. Its purpose ...
A Threshold Regularization Method for Inverse Problems
Inverse problems regularization oracle inequalities hard thresholding
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2011/6/16
A number of regularization methods for discrete inverse problems consist in considering weighted versions of the usual least square solution. However, these so-called filter methods are generally res...
Causal Network Inference via Group Sparse Regularization
Causal Network Inference via Group Sparse Regularization
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2011/7/5
This paper addresses the problem of inferring sparse causal networks modeled by multivariate auto-regressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure cons...
Implementing regularization implicitly via approximate eigenvector computation
Implementing regularization implicitly via approximate eigenvector computation
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2010/10/19
Regularization is a powerful technique for extracting useful information from noisy data. Typically, it is implemented by adding some sort of norm constraint to an objective function and then exactly...
Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation
Regularization regression comparing Bayesian frequentist methods
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2010/10/14
We propose a global noninformative approach for Bayesian variable selection that builds on Zellner's g-priors and is similar to Liang et al. (2008). Our proposal does not require any kind of calibrati...
Regularization in kernel learning
Regression reproducing kernel Hilbert space regulation leastsquares model selection
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2010/3/9
Under mild assumptions on the kernel, we obtain the best known
error rates in a regularized learning scenario taking place in the corresponding
reproducing kernel Hilbert space (RKHS). The main nove...