Descent-to-Delete: Gradient-Based Methods for Machine Unlearning

6 Jul 2020Seth NeelAaron RothSaeed Sharifi-Malvajerdi

We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both per-deletion run-time and steady-state error that do not grow with the length of the update sequence... (read more)

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