Variable Selection

Variable selection aims to identify the most relevant features from a dataset, improving model accuracy, interpretability, and computational efficiency. Current research emphasizes developing model-independent methods, addressing challenges posed by high-dimensional and noisy data, particularly in biomedical applications and time series forecasting. This involves exploring various algorithms, including Lasso, Bayesian methods, and tree-based approaches like random forests, often coupled with uncertainty quantification and techniques like knockoffs for controlling false discoveries. The impact of improved variable selection extends across diverse fields, enhancing model performance and facilitating more reliable scientific insights from complex datasets.

Papers

November 4, 2024

Variable Selection in Convex Piecewise Linear Regression
Haitham Kanj, Seonho Kim, Kiryung Lee
Variable Selection Non Asymptotic Analysis Max Affine

October 17, 2024

Ab initio nonparametric variable selection for scalable Symbolic Regression with large $p$
Shengbin Ye, Meng Li
High Dimensional Novel Regression Symbolic Regression Variable Selection Nonparametric Regression Ab Initio Non Parametric Learning Symbolic Approach

October 14, 2024

fastHDMI: Fast Mutual Information Estimation for High-Dimensional Data
Kai Yang, Masoud Asgharian, Nikhil Bhagwat, Jean-Baptiste Poline, Celia M.T. Greenwood
High Dimensional MAESTRO Dataset Mutual Information High Dimensional Data Variable Selection High Dimensional Space Variable Screening

September 28, 2024

Sparse Modelling for Feature Learning in High Dimensional Data
Harish Neelam, Koushik Sai Veerella, Souradip Biswas
Dimensionality Reduction Feature Extraction High Dimensional Data Feature Learning Sparse Model Variable Selection Sparse Optimization

September 13, 2024

Model-independent variable selection via the rule-based variable priority
Min Lu, Hemant Ishwaran
Model Free Predictive Performance Variable Selection Rule Based Permutation Importance

June 23, 2024

VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
Paul D. W. Kirk, Jackie Rao
High Dimensional Variational Inference Sequential Data Variable Selection Cancer Subtyping Soft QMIX Agglomerative Hierarchical Clustering Variational Mixture

May 5, 2024

Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
Jingbo Liu
Financial Application Core Stability Variable Selection LASSO Regression Lasso Problem Lasso Condition Number

April 7, 2024

CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
Anirban Mukherjee, Hannah Hanwen Chang
High Dimensional Causal Inference Categorical Data Variable Selection Robust Inference Categorical Encoding

February 26, 2024

Penalized Generative Variable Selection
Tong Wang, Jian Huang, Shuangge Ma
High Dimensional Variable Selection Wasserstein Generative Adversarial Network

February 2, 2024

kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
Practical Algorithm Variable Selection Dimensional Random Vector Variance Estimation KNN Algorithm

January 31, 2024

Variable selection for Na\"ive Bayes classification
Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo, M. Remedios Sillero-Denamiel
Many Sparse Sparsity Increase Variable Selection Localized Sparse Incomplete

January 18, 2024

False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Taulant Koka, Jasin Machkour, Michael Muma
Graph Learning Graphical Model Variable Selection Early Screening Graphical Lasso False Discovery Rate

January 9, 2024

Semi-Supervised Deep Sobolev Regression: Estimation, Variable Selection and Beyond
Zhao Ding, Chenguang Duan, Yuling Jiao, Jerry Zhijian Yang
Semi Supervised Learning Estimation Task Gradient Norm Variable Selection Sobolev Training

December 17, 2023

Random Forest Variable Importance-based Selection Algorithm in Class Imbalance Problem
Yunbi Nam, Sunwoo Han
Random Forest Class Imbalance Variable Selection Selection Policy Variable Importance Class Balancing Technique

November 2, 2023

Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
Kensuke Mitsuzawa, Motonobu Kanagawa, Stefano Bortoli, Margherita Grossi, Paolo Papotti
Maximum Mean Discrepancy Variable Selection Regularization Parameter Two Sample

September 25, 2023

Forecasting large collections of time series: feature-based methods
Li Li, Feng Li, Yanfei Kang
Time Series Forecasting Performance Variable Selection Time Series Feature Model Combination Feature Based Large Collection

September 13, 2023

Effect of hyperparameters on variable selection in random forests
Cesaire J. K. Fouodo, Lea L. Kronziel, Inke R. König, Silke Szymczak
Mixed Effect Random Forest Related Hyperparameters Variable Selection

September 7, 2023

Neural lasso: a unifying approach of lasso and neural networks
David Delgado, Ernesto Curbelo, Danae Carreras
Neural Network Machine Learning Cross Validation Variable Selection Unifying Theory Standard LASSO LASSO Regression Statistical Framework

August 30, 2023

SharpSAT-TD in Model Counting Competitions 2021-2023
Tuukka Korhonen, Matti Järvisalo
Variable Selection Model Counting

August 1, 2023

Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique
Junxian Zhu, Jin Zhu, Borui Tang, Xuanyu Chen, Hongmei Lin, Xueqin Wang
Sparse Model Generalized Linear Model Variable Selection Consistency Model Alternative Splicing Best Subset