arXiv (All)
open access
Conformal Robustness in Prediction-Driven Decision-Making
Modern prediction-driven decision systems often rely on black-box predictors, but a point forecast alone does not provide the uncertainty scale required for robust downstream decision-making. We build a score-calibrated robustness framework that converts any fixed point predictor into a decision-rel
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
- Оцінювання невизначеності та пояснюваності прогнозів у системах підтримки прийняття рішень на основі машинного навчання / Evaluating Prediction Uncertainty and Explainability in Machine Learning-Based Decision Support Systems#977882
- RL-PaO: Prediction as Action in Decision Making under Uncertainty#1132266
- Benchmarking non-conformity score functions in conformal prediction#998238
- An adaptive extension to robust data-driven predictive control under parametric uncertainty#1122970
- A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification#798417
Record · ID 1048915
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