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Found 6 papers, 5 papers with code
Coordinate Descent for SLOPE
The lasso is the most famous sparse regression and feature selection method.
Efficient methods for Gaussian Markov random fields under sparse linear constraints
Methods for inference and simulation of linearly constrained Gaussian Markov Random Fields (GMRF) are computationally prohibitive when the number of constraints is large.
The Hessian Screening Rule
Predictor screening rules, which discard predictors before fitting a model, have had considerable impact on the speed with which sparse regression problems, such as the lasso, can be solved.
The Strong Screening Rule for SLOPE
We develop a screening rule for SLOPE by examining its subdifferential and show that this rule is a generalization of the strong rule for the lasso.
Linear Mixed-Effects Models for Non-Gaussian Repeated Measurement Data
We consider the analysis of continuous repeated measurement outcomes that are collected through time, also known as longitudinal data.
Methodology
Whole-brain substitute CT generation using Markov random field mixture models
The amount of data needed to train a model for s-CT generation is of the order of 100 million voxels.
