arXiv (All)
open access
Spectral Dependence of Convex Regularization: Fundamental Limits under Right-Rotationally Invariant Designs
We study the fundamental limits of convex-regularized estimation in high-dimensional linear regression with right-rotationally invariant design matrices. We show that the asymptotic risks of convex-penalized least squares estimators are lower bounded by the risk of an approximate message passing alg
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A deterministic analysis of a class of nonlinear regression methods#796456
- Polynomial-Time Near-Optimal Estimation over Certain Type-2 Convex Bodies#971738
- Linear quasi-shrinkage estimator for high-dimensional optimization with linear constraints#1000452
- Sharp bounds for non-adaptive randomized approximation of high-dimensional vectors#1356139
- Randomized subspace correction methods for convex optimization#173322
Record · ID 1341502
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive