Variable Selection
127 papers with code • 0 benchmarks • 0 datasets
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Scalable Importance Tempering and Bayesian Variable Selection
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling.
Inference for $L_2$-Boosting
We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model errors, also known as $L_2$-Boosting.
Structured nonlinear variable selection
We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs.
Orthogonal Matching Pursuit for Text Classification
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential.
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics.
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.
Accurate Dictionary Learning with Direct Sparsity Control
Dictionary learning is a popular method for obtaining sparse linear representations for high dimensional data, with many applications in image classification, signal processing and machine learning.
Combinatorial Bayesian Optimization using the Graph Cartesian Product
QUVA-Lab/COMBO
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NeurIPS 2019
On this combinatorial graph, we propose an ARD diffusion kernel with which the GP is able to model high-order interactions between variables leading to better performance.
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
In addition, we introduce a specialized active-set strategy with gradient screening for avoiding costly gradient computations.
Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
We develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations.
