Phase transition and higher order analysis of \(L_q\) regularization under dependence
From MaRDI portal
Publication:6663352
DOI10.1093/imaiai/iaae005MaRDI QIDQ6663352
Hanwen Huang, Qing Long Yang, Peng Zeng
Publication date: 14 January 2025
Published in: Information and Inference: A Journal of the IMA (Search for Journal in Brave)
Cites Work
- Sparse recovery by non-convex optimization - instance optimality
- Some inequalities for Gaussian processes and applications
- Asymptotics for Lasso-type estimators.
- Overcoming the limitations of phase transition by higher order analysis of regularization techniques
- LASSO risk and phase transition under dependence
- Which bridge estimator is the best for variable selection?
- High-dimensional graphs and variable selection with the Lasso
- Restricted isometry properties and nonconvex compressive sensing
- Does $\ell _{p}$ -Minimization Outperform $\ell _{1}$ -Minimization?
- Precise Error Analysis of Regularized <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math> </inline-formula>-Estimators in High Dimensions
- Low noise sensitivity analysis of -minimization in oversampled systems
- Learning curves of generic features maps for realistic datasets with a teacher-student model*
- Does SLOPE outperform bridge regression?
- Living on the edge: phase transitions in convex programs with random data
- Optimization-Based AMP for Phase Retrieval: The Impact of Initialization and $\ell_{2}$ Regularization
- Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing
- On the Performance of Sparse Recovery Via $\ell_p$-Minimization $(0 \leq p \leq 1)$
- The Noise-Sensitivity Phase Transition in Compressed Sensing
- Sparse nonnegative solution of underdetermined linear equations by linear programming
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- The Lasso with general Gaussian designs with applications to hypothesis testing
- Fluctuations, bias, variance and ensemble of learners: exact asymptotics for convex losses in high-dimension
This page was built for publication: Phase transition and higher order analysis of \(L_q\) regularization under dependence