Variable Selection

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

Deep Knockoffs

msesia/deepknockoffs 16 Nov 2018

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

ds4dm/learn2branch NeurIPS 2019

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

Bolasso: model consistent Lasso estimation through the bootstrap

dmolitor/bolasso 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).

BART: Bayesian additive regression trees

JakeColtman/bartpy 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.

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

topipa/GP_varsel_KL_VAR 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.

Iteratively Reweighted $\ell_1$-Penalized Robust Regression

XiaoouPan/ILAMM 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.

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

isaac2math/solar_application 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.

Reversible Jump PDMP Samplers for Variable Selection

matt-sutton/rjpdmp 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.

Post-selection inference with HSIC-Lasso

tobias-freidling/hsic-lasso-psi 29 Oct 2020

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

Derandomizing Knockoffs

zhimeir/derandomKnock 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.