PURGE: Projected Unlearning via Retain-Guided Erasure
We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems. CL tries to learn new tasks without forgetting old ones; MU tries to erase specific data without hurting retained performance representing the same underlying tension in opposite directions. PURGE leverages this duality by adapting gradient projection from A-GEM (Chaudhry et al., 2019) so that every unlearning step is constrained to not increase the retain-set loss. On top of this, it performs multi-layer representation erasure, pushing forget-set activations in intermediate layers towards the reta...
| Authors | Vedant Jawandhia, Daksh Ahuja, Ghufran Alam Siddiqui et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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