arXiv (All)
open access
Optimal Recovery Meets Bayesian Learning: Where Worst-Case Bounds Pay Off
Worst-case Optimal Recovery (OR) and Bayesian learning describe the same Gaussian-quadratic-Hilbert problems in two vocabularies. We sharpen the correspondence - the radius of information equals a nugget-optimized GP posterior variance and is attained by the posterior mean at a closed-form balance n
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
- Training Neural Networks with Optimal Double-Bayesian Learning#204793
- Entropic optimal transport beyond product reference couplings: the Gaussian case on Euclidean space#128427
- Bayesian learning for neural networks: an algorithmic survey#19796
- Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks#131285
- Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression#177040
Record · ID 1077550
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