arXiv (All)
open access
High-dimensional sparse recovery from function samples Decoders, guarantees and instance optimality
We investigate the reconstruction of multivariate functions from samples using sparse recovery techniques. For Square Root Lasso, Orthogonal Matching Pursuit, and Compressive Sampling Matching Pursuit, we demonstrate both theoretically and empirically that they allow us to recover functions from a s
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
- Muon meets Tamed Langevin: Momentum Preconditioning beyond Convex and gradient-Lipschitz Potentials#1161027
- Muon meets Tamed Langevin: Momentum Preconditioning beyond Convex and gradient-Lipschitz Potentials#1157506
- Wideband Compressed-Domain Cramér-Rao Bounds for Near-Field XL-MIMO: Data and Geometric Diversity Decomposition#665702
- Wideband Compressed-Domain Cramér-Rao Bounds for Near-Field XL-MIMO: Data and Geometric Diversity Decomposition#664202
- Scaling Bayesian Bandit Encoding with Shared Learning#973879
Record · ID 1131386
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