arXiv Biology
open access
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
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
- Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization#174005
- Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization#173427
- Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization#173177
- Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits#1076374
- Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration#467142
Record · ID 172161
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