arXiv (All)
open access
Greedy sampling designs via reduced basis methods: optimal recovery in the uniform norm
We study optimal sampling recovery in reproducing kernel Hilbert spaces (RKHS) in the uniform norm. For every RKHS with bounded kernel, we establish new comparisons between linear sampling widths and Gelfand widths that overcome the known square-root gap, without requiring a measure or a Christoffel
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
- Sampling bias corrections for discrete and Gaussian partial information decompositions#1110855
- Sampling bias corrections for discrete and Gaussian partial information decompositions.#975473
- A Nearly Quadratic Lower Bound for Linear Optimization over Convex Bodies in the Membership Oracle Model#1078286
- The mod-minimizer: a simple and efficient sampling algorithm for long k-mers#10110
- Verifying Sampling Algorithms via Distributional Invariants / Daniel Zilken, Kevin Batz, Joost-Pieter Katoen, Tobias Winkler#780428
Record · ID 821603
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