Advances in Domain Adaptation Theory

Abstract : All machine learning algorithms that correspond to supervised and semi-supervised learning work under a common assumption: training and test data follow the same distribution. When the distribution changes, most statistical models must be reconstructed from new data that may be costly or even impossible to get for some applications. It therefore becomes necessary to develop approaches that reduce the need for obtaining new labeled samples. This is accomplished by exploiting data available in related areas and using it further in similar fields. This has given rise to a new family of machine learning algorithms called transfer learning: a learning setting inspired by the capability of a human being to extrapolate knowledge across tasks to learn more efficiently. This book provides an overview of the state-of-the-art theoretical results in a specific – and arguably the most popular – subfield of transfer learning called domain adaptation.
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Contributor : Emilie Morvant <>
Submitted on : Friday, September 13, 2019 - 3:34:19 PM
Last modification on : Sunday, September 15, 2019 - 1:13:19 AM


  • HAL Id : hal-02286281, version 1


Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani. Advances in Domain Adaptation Theory. ⟨Elsevier; ISTE⟩, 2019, 9781785482366. ⟨hal-02286281⟩



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