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Variational Bayesian Unlearning
[article]
2020
arXiv
pre-print
This paper studies the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We frame this problem as one of minimizing the Kullback-Leibler divergence between the approximate posterior belief of model parameters after directly unlearning from erased data vs. the exact posterior belief from retraining with remaining data. Using the variational inference (VI) framework, we show that it is equivalent to minimizing an evidence upper bound which
arXiv:2010.12883v1
fatcat:2jfmbvhyk5eitlhpqnh5k7vlda