arXiv (All)
open access
Multi-Task Active Learning with Efficient Resource Allocation
Many scientific studies allow costly auxiliary information to be collected during data labeling but not at deployment. Examples include diagnostic tests, laboratory assays, and expert evaluations. We study prediction under this deployment asymmetry, where auxiliary variables are selectively acquired
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
- Task-Oriented Active Learning of Residual Dynamics for Model Predictive Path Integral Control#1013974
- Active Inference with People: a general approach to real-time adaptive experiments#793617
- Active Inference with People: a general approach to real-time adaptive experiments#789613
- Active learning#1302058
- Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems#1198914
Record · ID 1216420
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