# Variable Selection

97 papers with code • 0 benchmarks • 0 datasets

## Benchmarks

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## Libraries

Use these libraries to find Variable Selection models and implementations## Most implemented papers

# Deep Knockoffs

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

# Exact Combinatorial Optimization with Graph Convolutional Neural Networks

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

# Bolasso: model consistent Lasso estimation through the bootstrap

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

# BART: Bayesian additive regression trees

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.

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

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.

# Iteratively Reweighted $\ell_1$-Penalized Robust Regression

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.

# 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.

# Reversible Jump PDMP Samplers for Variable Selection

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.

# Post-selection inference with HSIC-Lasso

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

# Derandomizing Knockoffs

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.