Variable Selection

116 papers with code • 0 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?


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

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.

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.

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).

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.

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.

abess: A Fast Best Subset Selection Library in Python and R

abess-team/abess-A-Fast-Best-Subset-Selection-Library-in-Python-and-R 19 Oct 2021

In addition, a user-friendly R library is available at the Comprehensive R Archive Network.