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Found 8 papers, 3 papers with code
FAStEN: an efficient adaptive method for feature selection and estimation in high-dimensional functional regressions
Functional regression analysis is an established tool for many contemporary scientific applications.
A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression
Feature Selection and Functional Data Analysis are two dynamic areas of research, with important applications in the analysis of large and complex data sets.
Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff
In this paper we propose $\epsilon$-Consistent Mixup ($\epsilon$mu).
Automated and Distributed Statistical Analysis of Economic Agent-Based Models
We propose a novel approach to the statistical analysis of stochastic simulation models and, especially, agent-based models (ABMs).
An Efficient Semi-smooth Newton Augmented Lagrangian Method for Elastic Net
Our new algorithm exploits both the sparsity induced by the Elastic Net penalty and the sparsity due to the second order information of the augmented Lagrangian.
On the bias of H-scores for comparing biclusters, and how to correct it
In the last two decades several biclustering methods have been developed as new unsupervised learning techniques to simultaneously cluster rows and columns of a data matrix.
Efficient and Effective $L_0$ Feature Selection
Because of continuous advances in mathematical programing, Mix Integer Optimization has become a competitive vis-a-vis popular regularization method for selecting features in regression problems.
Methodology
Linear Contour Learning: A Method for Supervised Dimension Reduction
We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of negligible variation for the response surface.
