arXiv (All)
open access
Erased, but Not Gone: Output Forgetting Is Not True Forgetting
Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced membership inference. But if output-level success can coexist with retraining-inconsistent residuals in representation space, what kind of forgetting are current evaluations actually certifyin
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
- Machine unlearning using forgetting neural networks#405948
- Less Noise, Same Certificate: Retain Sensitivity for Unlearning#1328398
- Memory-Coupled Unlearning for Data-Absent Identity Forgetting#1324749
- Why Forget-Only Unlearning Needs Memorization#1234944
- Why Forget-Only Unlearning Needs Memorization#1227315
Record · ID 1297182
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