arXiv (All)
open access
From Mathematical to Executable Certificates for Machine Unlearning
Machine unlearning is needed when data must be removed because of deletion requests, outdated records, or data-quality concerns, while retraining from scratch can be costly. Certified machine unlearning methods provide mathematical guarantees, while deployed systems release concrete finite-precision
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
- Less Noise, Same Certificate: Retain Sensitivity for Unlearning#1328398
- Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees#1343191
- Subtract the Corruption: Training-Data-Free Corrective Machine Unlearning using Task Arithmetic#1116170
- A Review on Machine Unlearning#964695
- Source-Free Class Relearning: Diagnosing Forgetting in Class Unlearning#822426
Record · ID 1186582
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