Principled Approaches for Private Adaptation from a Public Source

Raef Bassily, Mehryar Mohri, Ananda Theertha Suresh

Research output: Contribution to journalConference articlepeer-review


A key problem in a variety of applications is that of domain adaptation from a public source domain, for which a relatively large amount of labeled data with no privacy constraints is at one's disposal, to a private target domain, for which a private sample is available with very few or no labeled data. In regression problems, where there are no privacy constraints on the source or target data, a discrepancy minimization approach was shown to outperform a number of other adaptation algorithm baselines. Building on that approach, we initiate a principled study of differentially private adaptation from a source domain with public labeled data to a target domain with unlabeled private data. We design differentially private discrepancy-based adaptation algorithms for this problem. The design and analysis of our private algorithms critically hinge upon several key properties we prove for a smooth approximation of the weighted discrepancy, such as its smoothness with respect to the ℓ1-norm and the sensitivity of its gradient. We formally show that our adaptation algorithms benefit from strong generalization and privacy guarantees.

Original languageEnglish (US)
Pages (from-to)8405-8432
Number of pages28
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023 - Valencia, Spain
Duration: Apr 25 2023Apr 27 2023

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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