@inproceedings{f6adaab9be9d4be2b24982ea678cbbbf,
title = "A Unified PAC-Bayesian Framework for Machine Unlearning via Information Risk Minimization",
abstract = "Machine unlearning refers to mechanisms that can remove the influence of a subset of training data upon request from a trained model without incurring the cost of re-Training from scratch. This paper develops a unified PAC-Bayesian framework for machine unlearning that recovers the two recent design principles-variational unlearning [1] and forgetting Lagrangian [2] as information risk minimization problems [3]. Accordingly, both criteria can be interpreted as PAC-Bayesian upper bounds on the test loss of the unlearned model that take the form of free energy metrics.",
keywords = "Machine unlearning, PAC-Bayesian bounds, free energy minimization",
author = "Jose, {Sharu Theresa} and Osvaldo Simeone",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021 ; Conference date: 25-10-2021 Through 28-10-2021",
year = "2021",
doi = "10.1109/MLSP52302.2021.9596170",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
booktitle = "2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021",
address = "United States",
}