Multiple-source adaptation theory and algorithms – addendum

Judy Hoffman, Mehryar Mohri, Ningshan Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

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)
JournalAnnals of Mathematics and Artificial Intelligence
DOIs
StateAccepted/In press - 2022

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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