Unlearning Tabular Data Without a "Forget Set''
Keywords: machine unlearning, tabular representation learning, tabnet, tabular learning, attention, feature unlearning, tabular data
TL;DR: Efficient machine unlearning on tabular data without requiring access to a forget set.
Abstract: Machine unlearning is the process of removing the influence of some subset of the
training data from the parameters of a previously-trained model. Existing methods
typically require direct access to the “forget set" – the subset of training data to be
forgotten by the model. This limitation impedes privacy, as organizations need to
retain user data for the sake of unlearning when a request for deletion is made, rather
than being able to delete it immediately. We introduce RELOAD, an approximate
unlearning algorithm that leverages ideas from gradient-based unlearning and
neural network sparsity to achieve blind unlearning in settings of tabular data. The
method serially applies an ascent step with targeted parameter re-initialization and
fine-tuning, and on empirical unlearning tasks, RELOAD often approximates the
behaviour of a from-scratch retrained model better than approaches that leverage
the forget set. Empirical results highlight how RELOAD has the potential to improve
privacy-preserving machine learning in the tabular setting
Submission Number: 55
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