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Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
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...
Efficiently Using Second Order Information in Large l1 Regularization Problems
Efficiently Using Second Order Information Large l1 Regularization Problems
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
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
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
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
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
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
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
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
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
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...
We consider the problem of reconstructing a low
rank matrix from noisy observations of a subset of its entries.
This task has applications in statistical learning, computer vision,
and signal proce...
Adaptive complexity regularization for linear inverse problems
Inverse Problems Adaptive Estimation Regularization
2009/9/16
We tackle the problem of building adaptive estimation procedures for ill-posed inverse problems. For general regularization methods depending on tuning parameters, we construct a penalized method that...
Variable selection for multicategory SVM via adaptive sup-norm regularization
Classification L1-norm penalty multicategory sup-norm SVM
2009/9/16
Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatica...
A new approach to Cholesky-based covariance regularization in high dimensions
new approach Cholesky-based covariance regularization high dimensions
2010/3/18
In this paper we propose a new regression interpretation of the Cholesky factor of the covariance matrix, as opposed to the well known regression interpretation of the Cholesky factor of the inverse c...