arXiv (All)
open access
Beyond Source-Level Transfer: Sample-Level Learning for High-Dimensional Quantile Regression
This paper studies transfer learning for high-dimensional quantile regression with limited target samples and heterogeneous source domains. We propose a sample-level transfer learning method (SL-TL) that combines sample selection and importance weighting. Unlike existing source-level approaches that
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
- Transfer Learning with Heterogeneous Feature Spaces in Linear Regression#985872
- CrossEdit: Cross-Modal Training Enables Rich Audio-Visual Editing#1234631
- Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy#134520
- Emergence of Transfer Learning towards Specific Identification of Alzheimer's Disease A Prospective Approach#609164
- What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification#821954
Record · ID 1234089
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