arXiv (All)
open access
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment. Despite this importance, existing approaches are often heuristic and lack formal guarantees. In this paper, we analyze the fun
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
- On the Recoverability of Private Information Unlearning in Large Language Models#799191
- On the Recoverability of Private Information Unlearning in Large Language Models#797768
- Forget Me Not? Machine Unlearning's Implications for Privacy Law#1177212
- The Data Airlock: infrastructure for restricted data informatics#1178303
- Minimax Gaussian Mechanisms for Continual Machine Unlearning#1248908
Record · ID 1232584
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