arXiv (All)
open access
Selection from Hierarchical Data with Conformal e-values
Distribution-free predictive inference beyond the construction of prediction sets has gained a lot of interest in recent applications. One such application is the selection task, where the objective is to design a reliable selection rule to pick out individuals with desired unobserved outcomes while
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
- Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction#432532
- Learning and Selection Processes#117469
- Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion#941662
- Physics-Informed Support Vector Kernels via Green-Function Analogies and Jackson-Chebyshev Spectral Design#1013399
- Beyond Prediction: Conformal Inference for Latent Distributional Parameters#925216
Record · ID 1447990
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