arXiv (All)
open access
No-Regret Bayesian Optimization with Finite-Library Input-Warped Kernels
Gaussian-process Bayesian optimization (GP-BO) excels at black-box optimization of costly functions, e.g., hyperparameter optimization (HPO) and multi-agent system (MAS) design. Convergence-rate guarantees exist for select methods, notably GP upper confidence bound (GP-UCB), but require a fixed kern
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
- No-Regret Bayesian Optimization with Finite-Library Input-Warped Kernels#921130
- Online Adaptive Kernel Mixing for Gaussian Process Decision Making#1014938
- Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models#1123803
- Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models#919381
- Synchronous Monte Carlo method and its application to a mean-field spin glass model#1143391
Record · ID 925366
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