Variable Selection
127 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Variable Selection models and implementationsMost implemented papers
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
ds4dm/learn2branch
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NeurIPS 2019
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.
Deep Knockoffs
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.
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.
Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture
kieranjwood/trading-momentum-transformer
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We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies.
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).
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
XiaoouPan/ILAMM
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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.
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.
abess: A Fast Best Subset Selection Library in Python and R
In addition, a user-friendly R library is available at the Comprehensive R Archive Network.
