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README

Zero-Shot Machine Unlearning

Official repo of the paper Zero-Shot Machine Unlearning accepted in IEEE Transactions on Information Forensics and Security

Description

We introduce the problem statement of unlearning with no training samples and propose two solutions for the same. Unlearning quality has been evaluated through various metrics including membership inference attacks and inversion attacks. Also, a new metric called Anamnesis Index (AIN) has been introduced.

Paper

Zero-Shot Machine Unlearning

BibTex

@article{chundawat2023zero, title={Zero-shot machine unlearning}, author={Chundawat, Vikram S and Tarun, Ayush K and Mandal, Murari and Kankanhalli, Mohan}, journal={IEEE Transactions on Information Forensics and Security}, year={2023}, publisher={IEEE} }

Core symbols most depended-on inside this repo

evaluate
called by 8
utils.py
entropy
called by 3
metrics.py
collect_prob
called by 3
metrics.py
training_step
called by 2
utils.py
attention
called by 2
unlearn.py
divergence
called by 2
unlearn.py
relearn_time
called by 2
metrics.py
accuracy
called by 1
utils.py

Shape

Method 25
Function 22
Class 11

Languages

Python100%

Modules by API surface

models.py27 symbols
unlearn.py14 symbols
utils.py8 symbols
metrics.py6 symbols
datasets.py3 symbols

For agents

$ claude mcp add zero-shot-unlearning \
  -- python -m otcore.mcp_server <graph>

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