Multiple-source adaptation theory and algorithms – addendum

Judy Hoffman, Mehryar Mohri, Ningshan Zhang

Research output: Contribution to journalArticlepeer-review


In this note, we present some key results complementing a previous manuscript (Hoffman et al., Ann. Math. Artif. Intell. 89(3-4), 237–270, 2021) dealing with the problem of multiple-source adaptation, a key learning problem in applications. In particular, we extend the theoretical results presented for the probability model to the case where estimated distributions are used, first by giving a guarantee that depends on the Rényi divergence of the target distribution and the family of mixtures of estimated distributions, next by generalizing that to a result that only depends on the Rényi divergence with respect to the family of mixtures of the exact source distributions.

Original languageEnglish (US)
Pages (from-to)569-572
Number of pages4
JournalAnnals of Mathematics and Artificial Intelligence
Issue number6
StatePublished - Jun 2022


  • Domain adaptation
  • Multiple-source adaptation
  • Rényi divergence
  • Transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


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