156 citations · 391 across the 43 of their papers we have counts for
6 papers · 1 filter
Model Selection by Loss Rank for Classification and Unsupervised Learning
Minh-Ngoc Tran, Marcus Hutter
Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further…
The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
Minh-Ngoc Tran
Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produce…
Variational approximation for heteroscedastic linear models and matching pursuit algorithms
David J. Nott, Minh-Ngoc Tran, Chenlei Leng
Modern statistical applications involving large data sets have focused attention on statistical methodologies which are both efficient computationally and able to deal with the scr…
The Predictive Lasso
Minh-Ngoc Tran, David Nott, Chenlei Leng
We propose a shrinkage procedure for simultaneous variable selection and estimation in generalized linear models (GLMs) with an explicit predictive motivation. The procedure estima…
Bayesian Adaptive Lasso
Chenlei Leng, Minh Ngoc Tran, David Nott
We propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression. The BaLasso is adaptive to the signal level by adopting dif…
Model Selection with the Loss Rank Principle
Marcus Hutter, Minh-Ngoc Tran
A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN…