
Our research introduces a novel approach that leverages influence functions and principles of distributional independence to address challenges in machine unlearning, ensuring privacy protection while maintaining model performance. By proposing a comprehensive framework for machine unlearning, we aim to navigate the intricate terrain of non-uniform feature and label removal.


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addressing-challenges-in-machine-unlearning-of-features-and-labels


Addressing Challenges in Machine Unlearning of Features and Labels


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Machine unlearning faces challenges from distributional shifts, but the DUI method offers an efficient and adaptable solution.


machine-unlearning-addressing-distributional-shifts-with-independence-criterion


Machine Unlearning: Addressing Distributional Shifts with Independence Criterion
