# Variable Selection

97 papers with code • 0 benchmarks • 0 datasets

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## Libraries

Use these libraries to find Variable Selection models and implementations
2 papers
67

# Deep Knockoffs

16 Nov 2018

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.

4

# Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.

3

# Bolasso: model consistent Lasso estimation through the bootstrap

8 Apr 2008

For various decays of the regularization parameter, we compute asymptotic equivalents of the probability of correct model selection (i. e., variable selection).

2

# BART: Bayesian additive regression trees

19 Jun 2008

We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior.

2

# Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

21 Dec 2017

Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance.

2

# Iteratively Reweighted $\ell_1$-Penalized Robust Regression

9 Jul 2019

This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonconvex regularization.

2

# Accuracy and stability of solar variable selection comparison under complicated dependence structures

30 Jul 2020

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.

2

# Reversible Jump PDMP Samplers for Variable Selection

22 Oct 2020

A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios.

2

# Post-selection inference with HSIC-Lasso

29 Oct 2020

Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning.

2

# Derandomizing Knockoffs

4 Dec 2020

Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives.

2