arXiv (All)
open access
Reliable learning in challenging environments
The problem of designing learners that provide guarantees that their predictions are provably correct is of increasing importance in machine learning. However, learning theoretic guarantees have only been considered in very specific settings. In this work, we consider the design and analysis of reli
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
- Learning-Augmented Robust Algorithmic Recourse#173226
- Position: A Score Should Travel With Its Repair History#1180130
- Position: A Score Should Travel With Its Repair History#1180035
- Learnable Randomization as Commitment Against Adaptive Optimizers#1117103
- Statistical Gems in AI: Toward Reliable and Efficient Intelligence#263689
Record · ID 1011949
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