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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...
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...
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...
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...
Covariance regularization by thresholding
Covariance estimation regularization sparsity thresholding large p smalln high dimension low sample size
2010/3/17
This paper considers regularizing a covariance matrix of p variables
estimated from n observations, by hard thresholding. We show
that the thresholded estimate is consistent in the operator norm as
...