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Publications
  • Mar 2021
  • Conference Presentation

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

By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
  • Format:Print
  • | Language:English
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Abstract

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. We also introduce several new conceptual distinctions: for example, we can ask that after a deletion, the entire state maintained by the optimization algorithm is statistically indistinguishable from the state that would have resulted had we retrained, or we can ask for the weaker condition that only the observable output is statistically indistinguishable from the observable output that would have resulted from retraining. We are able to give more efficient deletion algorithms under this weaker deletion criterion.

Keywords

Machine Learning; Unlearning Algorithm; Mathematical Methods

Citation

Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
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About The Author

Seth Neel

Technology and Operations Management
→More Publications

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More from the Authors
  • On the Privacy Risks of Algorithmic Recourse By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
  • Data Privacy in Practice at LinkedIn By: Iavor Bojinov, Marco Iansiti and Seth Neel
  • Adaptive Machine Unlearning By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
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