Dec 31, 2020 · In this paper, we propose a coded learning protocol where we utilize linear encoders to encode the training data into shards prior to the ...
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What is unlearning in machine learning?
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This repository contains the python code that sweeps the size of shards and plots the performance vs. unlearning cost tradeoff for uncoded machine unlearning ( ...
A sample can be perfectly unlearned if we retrain all models that used it from scratch with that sample removed from their training dataset. When multiple such ...
Jun 25, 2021 · In this paper, we propose a coded learning protocol where we utilize linear encoders to encode the training data into shards prior to the ...
In this study, we propose a novel amortized unlearning approach using codebook features and Sparse Autoencoders (SAEs).
Results show that the proposed coded machine unlearning provides a better performance versus unlearning cost trade-off compared to the uncoded baseline, ...
In our method, we train the model with mnemonic code; when forgetting, we use a small number of mnemonic codes to calculate the FIM and get the effective ...
Feb 21, 2024 · We formalize Corrective Machine Unlearning as the problem of mitigating the impact of data affected by unknown manipulations on a trained model, ...
Second, we show how to define an unlearning problem in machine learning systems. This includes the formulation of exact unlearning and approximate unlearning as ...
This repository contains the core code used in the SISA experiments of our Machine Unlearning paper along with some example scripts.