arXiv (All)
open access
Adaptive Bayesian Regression on Data with Low Intrinsic Dimensionality
We study how the posterior contraction rate under a Gaussian process (GP) prior depends on the intrinsic dimension of the predictors and the smoothness of the regression function. An open question is whether a generic GP prior that does not incorporate knowledge of the intrinsic lower-dimensional st
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
- Learn Only Where You Can't Compute: Structured Corrections to Gaussian Process Posteriors#1357632
- Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices#1104547
- Spanning Diffusion and Flow-based Posterior Sampling via Exact Gaussian Conditioning#1298308
- Spanning Diffusion and Flow-based Posterior Sampling via Exact Gaussian Conditioning#1291800
- A pedestrian's approach to Bayesian methods for inverse problems#1343973
Record · ID 1200726
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