mlsanityData to 7 Oct 2026

Lasso Meets Horseshoe : A Survey

arXiv 1706.10179 ↗First posted 30 Jun 2017stat.MEcited by 17

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Abstract (arXiv)

The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. Lasso is a gold standard for predictor selection while horseshoe is a state-of-the-art Bayesian estimator for sparse signals. Lasso is fast and scalable and uses convex optimization whilst the horseshoe is non-convex. Our novel perspective focuses on three aspects: (i) theoretical optimality in high dimensional inference for the Gaussian sparse model and beyond, (ii) efficiency and scalability of computation and (iii) methodological development and performance.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jun 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 0; 2025: 5; 2026: 4.

17 papers.

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  1. 2021Bounding Wasserstein distance with couplingscited by 5
  2. 2022Scalable Spike-and-Slabcited by 5
  3. 2023Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networkscited by 4
  4. 2021Variational Bayes for high-dimensional proportional hazards models with applications within gene expressioncited by 3
  5. 2025Optimal sparse phase retrieval via a quasi-Bayesian approachcited by 3
  6. 2022Sparse Bayesian Lasso via a Variable-Coefficient ℓ1 Penaltycited by 2
  7. 2021T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphscited by 1
  8. 2025Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identificationcited by 1
  9. 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
  10. 2025Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responsescited by 1
  11. 2021Decoupling Shrinkage and Selection for the Bayesian Quantile Regressioncited by 0
  12. 2022Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Batterycited by 0
5 more
  1. 2025False Discovery Rate Control via Frequentist-assisted Horseshoecited by 0
  2. 2026Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complicationscited by 0
  3. 2026Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methodscited by 0
  4. 2026Horseshoe Priors for Spatial Small Area Estimation: Regular Variation, Tail Robustness, and Deep Learningcited by 0
  5. 2026Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selectioncited by 0