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Found 7 papers, 0 papers with code
Rademacher upper bounds for cross-validation errors with an application to the lasso
We establish a general upper bound for $K$-fold cross-validation ($K$-CV) errors that can be adapted to many $K$-CV-based estimators and learning algorithms.
Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing
In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney.
Accuracy and stability of solar variable selection comparison under complicated dependence structures
In this paper we focus on the empirical variable-selection peformance of subsample-ordered least angle regression (Solar) -- a novel ultrahigh dimensional redesign of lasso -- on the empirical data with complicated dependence structures and, hence, severe multicollinearity and grouping effect issues.
$\left( β, \varpi \right)$-stability for cross-validation and the choice of the number of folds
The $\left( \beta, \varpi \right)$-stability mathematically connects the generalization ability and the stability of the cross-validated model via the Rademacher complexity.
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression
We propose using generalization error minimization (GEM) as a framework for model selection.
Finite-sample and asymptotic analysis of generalization ability with an application to penalized regression
We show that the error bounds may be used for tuning key estimation hyper-parameters, such as the number of folds $K$ in cross-validation.
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso
In this paper, we study model selection from the perspective of generalization ability, under the framework of structural risk minimization (SRM) and Vapnik-Chervonenkis (VC) theory.
