arXiv (All)
open access
TOBYQA: A Time-Augmented Model-Based Method for Derivative-Free Optimization under Noise and Temporal Drift
Derivative-free optimization (DFO) is challenging when the observation channel varies over time and evaluations are noisy. Conventional model-based methods assume stationary observations; under temporal drift, historical data biases gradient estimates and acceptance tests confound latent-objective d
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
- Revisiting Classifier-Free Guidance Methods in Latent Diffusion Models#625342
- Revisiting Classifier-Free Guidance Methods in Latent Diffusion Models#623069
- Deterministic Regime Switching and Feasibility Inversion in Dynamic Tensor Rematerialization#1105808
- Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusion#999861
- The Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives#985882
Record · ID 984225
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