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 ...
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 ...
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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 ...
This repository contains the python code that sweeps the size of shards and plots the performance vs. unlearning cost tradeoff for uncoded machine unlearning ( ...
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.
Results show that the proposed coded machine unlearning provides a better performance versus unlearning cost trade-off compared to the uncoded baseline, ...
A collection of academic articles, published methodology, and datasets on the subject of machine unlearning.
In this paper, we present an experimental study of the three state-of-the-art approximate unlearning methods for linear models.
This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine ...
May 24, 2024 · This paper introduces a methodology to align LLMs, such as Open Pre-trained Transformer Language Models, with ethical, privacy, and safety ...