A Sparse-Group Lasso

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957

Regularization Paths for Generalized Linear Models via Coordinate Descent

Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927

Model Selection and Estimation in Regression with Grouped Variables

Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261

The Group Lasso for Logistic Regression

Meier, L., van de Geer, S., & Bühlmann, P. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00627.x · Cited by 1,681

A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen

Ma, X.-J., Wang, Z., Ryan, P. D., Isakoff, S. J., Barmettler, A., Fuller, A. P., Muir, B., Mohapatra, G., Salunga, R. C., Tuggle, J. T., et al. (2004) · Cancer Cell · doi:10.1016/j.ccr.2004.05.015 · Cited by 959

NESTA: A Fast and Accurate First-Order Method for Sparse Recovery

Becker, S., Bobin, J., & Candès, E. J. (2009) · Cited by 897

Gradient methods for minimizing composite objective function

Nesterov, Y. E. (2007) · Cited by 867

Group lasso with overlap and graph lasso

Jacob, L., Obozinski, G., & Vert, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553431 · Cited by 842

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions

Su, T., Wang, Y., Liu, Y., Branton, W. G., Asahchop, E. L., Power, C., Jiang, B., Kong, L., & Tang, N. (2020) · Entropy · doi:10.3390/e22111257 · Cited by 2

Hierarchical Sparse Recovery from Hierarchically Structured Measurements with Application to Massive Random Access

Groß, B., Flinth, A., Roth, I., Eisert, J., & Wunder, G. (2021) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp49050.2021.9513765 · Cited by 2

Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing

Chen, K., Bu, Z., & Xu, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86523-8_31 · Cited by 2

New Results on Graphical Modeling of High-Dimensional Dependent Time Series

TUGNAIT, J. K. (2021) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf53345.2021.9723239 · Cited by 2

Decoding Genetic Markers of Multiple Phenotypic Layers Through Biologically Constrained Genome-To-Phenome Bayesian Sparse Regression

Deprez, M., Moreira, J., Maxime Sermesant, B., & Lorenzi, M. (2022) · Frontiers in Molecular Medicine · doi:10.3389/fmmed.2022.830956 · Cited by 2

Sparse regression with clustered predictors

Kenny, A., Solomon, D., Patwary, M. S. A., & Das, K. P. (2023) · Journal of Statistical Research · doi:10.3329/jsr.v56i1.63945 · Cited by 2

Brain space image reconstruction of functional near-infrared spectroscopy using a Bayesian adaptive fused sparse overlapping group lasso model

Zhai, X., Santosa, H., Krafty, R. T., & Huppert, T. J. (2023) · Neurophotonics · doi:10.1117/1.nph.10.2.023516 · Cited by 2

Object detection for blind inspection of industrial products based on neural architecture search

Huang, L., Deng, W., Li, C., & Yang, T. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02199-w · Cited by 2

On Exact Feature Screening in Ultrahigh-Dimensional Binary Classification

Roy, S., Sarkar, S., Dutta, S., & Ghosh, A. K. (2023) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2023.2270656 · Cited by 2

sparse-lm: Sparse linear regression models in Python

Barroso-Luque, L., & Xie, F. (2023) · The Journal of Open Source Software · doi:10.21105/joss.05867 · Cited by 2

Integrating Automatic Temporal Relation Graph into Multi-Task Learning for Alzheimer’s Disease Progression Prediction

Zhou, M., Wang, X., Zhang, Y., Liu, T., Liu, K., & Yang, P. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385873 · Cited by 2

Estimating Double Sparse Structures Over ℓu(ℓq) -Balls: Minimax Rates and Phase Transition

Li, Z., Zhang, Y., & Yin, J. (2024) · IEEE Transactions on Information Theory · doi:10.1109/tit.2024.3451512 · Cited by 2

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

ZHANG, M., Junliang, D. I. N. G., & Li, B. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.10.019 · Cited by 2

Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models

St‐Pierre, J., Oualkacha, K., & Bhatnagar, S. (2024) · Statistical Methods in Medical Research · doi:10.1177/09622802241293768 · Cited by 2

xIIRS: Industrial Internet Intrusion Response Based on Explainable Deep Learning

Xue, Q., Zhang, Z., Fan, K., & Wang, M. (2025) · Electronics · doi:10.3390/electronics14050987 · Cited by 2

On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series

TUGNAIT, J. K. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3578807 · Cited by 2

DrFARM: identification of pleiotropic genetic variants in genome-wide association studies

Chan, L. S., Li, G., Fauman, E. B., Yin, X., Laakso, M., Boehnke, M., & Song, P. X. ‐. (2025) · Nature Communications · doi:10.1038/s41467-025-60439-4 · Cited by 2

Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies

Li, Y., O’Connor, G. T., Dupuis, J., & Kolaczyk, E. D. (2015) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2014-0073 · Cited by 2

Change-point estimation of high-dimensional streaming data via sketching

Chi, Y., & Wu, Y. (2015) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2015.7421091 · Cited by 2

Parallel block coordinate minimization with application to group regularized regression

Calafiore, G. C. (2016) · Optimization and Engineering · doi:10.1007/s11081-016-9336-z · Cited by 2

Heterogeneous representation learning with separable structured sparsity regularization

Yang, P., Tan, Q., Zhu, Y., & He, J. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1094-5 · Cited by 2

Penalization methods with group-wise sparsity: econometric applications to eBay Motors online auctions

Wang, Q., & Zhao, D. (2018) · Empirical Economics · doi:10.1007/s00181-018-1460-5 · Cited by 2

Incremental Learning System for Disease Prediction: Epileptic Seizure Case

Zhang, H., Wu, D., Zhang, R., & Wang, R. (2018) · Globecom · doi:10.1109/glocom.2018.8647373 · Cited by 2

Addition of Pathway-Based Information to Improve Predictions in Transcriptomics

Urda, D., Veredas, F. J., Turias, I. J., & Franco, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17935-9_19 · Cited by 2

Group-sparse matrix recovery

Zeng, X., & Figueiredo, M. A. T. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854988 · Cited by 2

Search for relevant subsets of binary predictors in high dimensional regression for discovering the lead molecule

Mameli, V., Slanzi, D., Poli, I., & Green, D. V. S. (2021) · Pharmaceutical Statistics · doi:10.1002/pst.2117 · Cited by 2

An enhanced feature selection and cancer classification for microarray data using relaxed Lasso and support vector machine

Mazlan, A. U., Sahabudin, N. A., Remli, M. A., Ismail, N. S. N., & Adenuga, K. I. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89824-9.00016-1 · Cited by 2

Integrative analysis of multiple types of genomic data using an accelerated failure time frailty model

Deng, S., Chen, J., & Shi, H. (2021) · Computational Statistics · doi:10.1007/s00180-020-01060-5 · Cited by 2

Sparse parameter identification of stochastic dynamical systems

Zhao, W. (2022) · Control Theory and Technology · doi:10.1007/s11768-021-00077-5 · Cited by 2

Linear Convergence of Prox-SVRG Method for Separable Non-smooth Convex Optimization Problems under Bounded Metric Subregularity

Zhang, J., & Zhu, X. (2022) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-021-01978-w · Cited by 2

Multivariate Functional Kernel Machine Regression and Sparse Functional Feature Selection

Naiman, J., & Song, P. X. ‐. (2022) · Entropy · doi:10.3390/e24020203 · Cited by 2

Sparse-Input Neural Networks to Differentiate 32 Primary Cancer Types on the Basis of Somatic Point Mutations

Δικαίος, Ν. (2022) · Onco · doi:10.3390/onco2020005 · Cited by 4

Portfolio Selection: A Statistical Learning Approach

Peng, Y., & Linetsky, V. (2022) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3533271.3561707 · Cited by 2

Multi-task sparse identification for closed-loop systems with general observation sequences

Zhang, K., Luan, X., Ping, X., & Liu, F. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2023.04.024 · Cited by 2

A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants

Chekouo, T., Stingo, F. C., Mohammed, S., Rao, A. U. K., & Baladandayuthapani, V. (2023) · The Annals of Applied Statistics · doi:10.1214/23-aoas1749 · Cited by 2

ALRIGMR: Adaptive logistic regression via integrating gene mutation and RNA-seq for liver cancer diagnosis

Li, J., Cao, F., & Zhang, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106025 · Cited by 2

A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction

Milzarek, A., Schaipp, F., & Ulbrich, M. (2024) · SIAM Journal on Optimization · doi:10.1137/22m1488181 · Cited by 2

Regularization Approaches for High‐Dimensional Cox Models: A Case Study in Triple‐Negative Breast Cancer

González-Barquero, P., Lillo, R. E., & Méndez-Civieta, Á. (2026) · International Statistical Review · doi:10.1111/insr.70042 · Cited by 2

Signature Selection for Grouped Features with a Case Study on Exon Microarrays

Lee, S. (2014) · Studies in computational intelligence · doi:10.1007/978-3-662-45620-0_14 · Cited by 2

SPARSE REGULARIZATION FOR BI-LEVEL VARIABLE SELECTION

Matsui, H. (2015) · Journal of the Japanese Society of Computational Statistics · doi:10.5183/jjscs.1502001_216 · Cited by 2

A Mixed $$(\mathbf 2 ,{\varvec{p}}$$ ( 2 , p -like)-Norm Penalized Least Mean Squares Algorithm for Block-Sparse System Identification

Wei, Y., Zhang, Y., Wang, C., & Zheng, Y. R. (2018) · Circuits Systems and Signal Processing · doi:10.1007/s00034-018-0769-9 · Cited by 2

MCEN: a method of simultaneous variable selection and clustering for high-dimensional multinomial regression

Ren, S., Kang, E. L., & Lu, J. L. (2019) · Statistics and Computing · doi:10.1007/s11222-019-09880-2 · Cited by 2

A CNN channel pruning low-bit framework using weight quantization with sparse group lasso regularization

Long, X., Zeng, X., Liu, Y., Xiao, H., Zhang, M., & Ben, Z. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-191014 · Cited by 2

Informative Clusters for Multivariate Extremes.

Jalalzai, H., & Leluc, R. (2020) · Cited by 2

On the Adversarial Robustness of LASSO Based Feature Selection

Li, F., Lai, L., & Cui, S. R. (2022) · Wireless networks · doi:10.1007/978-3-031-16375-3_3 · Cited by 2

Period Estimation For Incomplete Time Series

Zhang, L., & Bogdanov, P. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00016 · Cited by 2

Computational Probing the Methylation Sites Related to EGFR Inhibitor-Responsive Genes

Yuan, R., Chen, S., & Wang, Y. (2021) · Biomolecules · doi:10.3390/biom11071042 · Cited by 2

Chunk Dynamic Updating for Group Lasso with ODEs

Li, D., & Gu, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20704 · Cited by 2

Benchmarking Penalized Regression Methods in Machine Learning for Single Cell RNA Sequencing Data

Puliparambil, B. S., Tomal, J., & Yan, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06220-9_17 · Cited by 2

Improving Group Lasso for High-Dimensional Categorical Data

Nowakowski, S., Pokarowski, P., Rejchel, W., & Sołtys, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36021-3_47 · Cited by 2

Sparse-Group Boosting: Unbiased Group and Variable Selection

Obster, F., & Heumann, C. (2024) · The American Statistician · doi:10.1080/00031305.2024.2408007 · Cited by 2

In pursuit of high resolution radar using pursuit algorithms

Maud, A. R. (2015) · Purdue e-Pubs (Purdue University System) · Cited by 2

$\ell_1$ Adaptive Trend Filter via Fast Coordinate DescentPreprint

Souto, M., Garcia, J. D., & do Amaral, G. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.03799 · Cited by 2

A Communication-Efficient Parallel Method for Group-LassoPreprint

Chen, B., & Zhu, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02222 · Cited by 2

Graph-Sparse Logistic RegressionPreprint

LeNail, A., Schmidt, L., Li, J., Ehrenberger, T., Sachs, K., Jegelka, S., & Fraenkel, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.05510 · Cited by 2

Information Projection and Approximate Inference for Structured Sparse VariablesPreprint

Khanna, R., Ghosh, J., Poldrack, R. A., & Koyejo, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.03204 · Cited by 2

Hierarchical sparse recovery from hierarchically structured measurements.Preprint

Groß, B., Flinth, A., Roth, I., Eisert, J., & Wunder, G. (2020) · arXiv (Cornell University) · Cited by 2

Structure Adaptive Elastic-NetPreprint

Pramanik, S., & Zhang, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02041 · Cited by 2

The Complete Lasso Tradeoff DiagramPreprint

Wang, H., Yang, Y., Bu, Z., & Su, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11078 · Cited by 2

Asgl: A Python Package for Penalized Linear and Quantile Regression

Méndez-Civieta, Á., Aguilera‐Morillo, M. C., & Lillo, R. E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.00472 · Cited by 2

Compressed Lightweight Deep Learning Models for Resource-Constrained Iot Devices in Healthcare SectorPreprint

Malik, I. A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4185661 · Cited by 2

Factor Augmented Matrix RegressionPreprint

Chen, E., Fan, J., & Zhu, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4856343 · Cited by 2

Regularized and robust regression methods for high dimensional data

Hashem, H. A. (2014) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 2

Readability prediction: How many features are necessary?

Schwendinger, F., Vana, L., & Hornik, K. (2024) · The Annals of Applied Statistics · doi:10.1214/23-aoas1820 · Cited by 1

From bench to bedside: Practical considerations for translational studies using single-cell mass cytometry

Stelzer, I. A., Feyaerts, D., Ganio, E. A., Amar, J. N., Hedou, J. J., Tsai, A. S., & Gaudilliere, B. L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-822213-3.00002-4 · Cited by 1

Adaptive peak price with lazy updates for short-term portfolio optimization

Xie, K., & Chu, Y. (2024) · Discover Data · doi:10.1007/s44248-024-00017-z · Cited by 1

Molecular group and correlation guided structural learning for multi-phenotype prediction

Zhou, X., Cai, M., Yue, M., Celedon, J. C., Wang, J., Ding, Y., Chen, W., & Li, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae585 · Cited by 1

Subgroup learning for multiple mixed-type outcomes with block-structured covariates

Zhao, X., Tang, L., Zhang, W., & Zhou, L. (2024) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2024.108105 · Cited by 1

GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model

Li, J., Xu, Y., Ma, S., & Fang, K. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf481 · Cited by 1

Exploring multi-scale time group for common spatial pattern feature based motor imagery EEG classification

Mi, J., Li, R., Liu, K., & Li, W. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108591 · Cited by 1

A Block Sparsity-Based Method for Target Detection by Synthetic Aperture Interferometric Radiometer

Min, J., Zhu, D., & Hu, F. (2025) · Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC) · doi:10.1109/csrswtc67757.2025.11384373 · Cited by 1

PACells identifies phenotype-associated cell states from single-cell chromatin accessibility profiles

Hua, J., Sheng, Q., Xiao, S., Yang, Z., Qi, J., & Jin, S. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.03.012 · Cited by 1

Approximate Message Passing for Multi-Preamble Detection in OTFS Random Access

Mirri, A., Kunde, V. T., Paolini, E., & Chamberland, J. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460473 · Cited by 1

Structured Iterative Hard Thresholding for Categorical and Mixed Data Types

Nguyen, T., & Obafemi-Ajayi, T. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9002948 · Cited by 1

Numerical Tolerance for Spectral Decompositions of Random Matrices and Applications to Network Inference

Athreya, A., Lubberts, Z., Priebe, C. E., Park, Y., Tang, M., Lyzinski, V., Kane, M. J., & Lewis, B. W. (2022) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2022.2082972 · Cited by 1

Variance-Guided Structured Sparsity in Deep Neural Networks

Pandit, M. K., & Banday, M. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3221688 · Cited by 1

Variable selection using the EM and CEM algorithms in mixtures of linear mixed models

Novais, L., & Faria, S. (2023) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2023.2176503 · Cited by 1

Functional Adaptive Double-Sparsity Estimator for Functional Linear Regression Model with Multiple Functional Covariates

Cao, C., Cao, J., Wang, H., Tsui, K., & Li, X. (2024) · Statistica Sinica · doi:10.5705/ss.202023.0091 · Cited by 1

An Appraisal and Operating Characteristics of Major ML Methods Applicable in Healthcare and Health Science

Simon, G., & Aliferis, C. (2024) · Health informatics · doi:10.1007/978-3-031-39355-6_3 · Cited by 1

Bayesian thresholded modeling for integrating brain node and network predictors

Sun, Z., Xu, W., Li, T., Kang, J., Alanis‐Lobato, G., & Zhao, Y. (2024) · Biostatistics · doi:10.1093/biostatistics/kxae048 · Cited by 1

Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network

Luo, B., & Halabi, S. (2025) · Bioengineering · doi:10.3390/bioengineering12060596 · Cited by 1

Sparse outlier-robust PCA for multi-source data

Puchhammer, P., Wilms, I., & Peter Filzmoser, A. (2025) · Statistics and Computing · doi:10.1007/s11222-025-10771-y · Cited by 1

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

Omer, T., Månsson, K., Sjölander, P., & Uddin, G. S. (2026) · Journal of Forecasting · doi:10.1002/for.70107 · Cited by 1

Bayesian feature selection in joint models with application to a cardiovascular disease cohort study

Islam, M., Daniels, M. J., Aghabazaz, Z., & Siddique, J. (2026) · Statistical Methods in Medical Research · doi:10.1177/09622802251414939 · Cited by 1

Exact recovery in the double sparse model: Sufficient and necessary signal conditions

Liu, S., Li, Z., Zhang, Y., & Yin, J. (2026) · Electronic Journal of Statistics · doi:10.1214/26-ejs2486 · Cited by 1

Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data

Zhong, C., Jiang, S., & Wei, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag082 · Cited by 1

Challenges and Opportunities for Statistics in Omics Data Analysis

Carrasquinha, E., Sousa, L., & Silva, C. A. (2026) · Advances in experimental medicine and biology · doi:10.1007/978-3-032-18966-0_10 · Cited by 1

ESG-XAI: An explainable unsupervised feature selection pipeline for ESG datasets

Lim, T., Teng, Y., & Wang, Z. (2026) · Data Science in Finance and Economics · doi:10.3934/dsfe.2026010 · Cited by 1

Multi-target Tracking with Sparse Group Features and Position Using Discrete-Continuous Optimization

Peralta, B., & Soto, Á. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16634-6_49 · Cited by 1

Image Segmentation Using Dictionary Learning and Compressed Random Features

Bull, G., Gao, J., & Antolovich, M. (2014) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2014.7008112 · Cited by 1

Learning Gaussian Graphical Models Using Discriminated Hub Graphical Lasso

Li, Z., Bai, J., & Zhou, W. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462390 · Cited by 1

Identifying and quantifying nonlinear structured relationships in complex manufactural systems

Xu, T., Yan, T., Song, D., Cheng, W. D., Chen, H., Jiang, G., & Bi, J. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258067 · Cited by 1

On Data Integration Problems With Manifolds

Culp, M. V., Ryan, K. J., Banerjee, P., & Morehead, M. (2018) · Technometrics · doi:10.1080/00401706.2018.1482788 · Cited by 1

Hierarchical Sparsity Within And Across Overlapping Groups

Bayram, İ. (2018) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp.2018.8450707 · Cited by 1

Learning Variables Structure Using Evolutionary Algorithms to Improve Predictive Performance

Nimo, D., Dorronsoro, B., Turias, I. J., & Urda, D. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-41913-4_6 · Cited by 1

Robust Variant Target Recognition Based on Structured Sparse Representation for Radar HRRP Data

Wang, P., Meng, Y., Zhu, W., Song, X., Ding, J., & Liu, H. (2019) · International Radar Conference (RADAR) · doi:10.1109/radar41533.2019.171264 · Cited by 1

Multiple-signal Synchronous Change-point Detection by Piecewise Linear Regression with Group Lasso Constraints

Yang, L., & Li, Y. (2020) · IEEE International Conference on Industrial Engineering and Applications (ICIEA) · doi:10.1109/iciea49774.2020.9101941 · Cited by 1

Fast Block Coordinate Descent for Sparse Group Lasso

Ida, Y., Fujiwara, Y., & Kashima, H. (2020) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.36-1_a-jb1 · Cited by 1

Sparse group regularization for semi‐continuous transportation data

Feng, T., & Boyle, L. N. (2021) · Statistics in Medicine · doi:10.1002/sim.8942 · Cited by 1

Objective Bayesian group variable selection for linear model

Kang, S. G., Lee, W. D., & Kim, Y. (2021) · Computational Statistics · doi:10.1007/s00180-021-01160-w · Cited by 1

Sparse Group Selection Through Co-Adaptive PenaltiesPreprint

Fang, Z. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1111.4416 · Cited by 2

A note on the group lasso and a sparse group lassoPreprint

Friedman, J. H., Hastie, T., & Tibshirani, R. J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.0736 · Cited by 645

Sparse optimization for nonconvex group penalized estimation

Lee, S., Oh, M., & Kim, Y. (2015) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2015.1026822 · Cited by 11

Sparse Group Selection Through Co-Adaptive Penalties Zhou FangPreprint

(2011)

Constrained Grouped Sparsity

Guo, Y., Gao, J., & Hong, X. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35101-3_37 · Cited by 1