Domain Adaptation Theory: Available Theoretical Results by Ievgen RedkoDomain Adaptation Theory: Available Theoretical Results by Ievgen Redko

Domain Adaptation Theory: Available Theoretical Results

byIevgen Redko, Emilie Morvant, Amaury Habrard

Hardcover | December 1, 2018

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Domain Adaptation Theory: Available Theoretical Resultsgives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version.

Additional sections present generalization guarantees based on the robustness and stability properties of the learning algorithm.



  • Gives an overview of current results on transfer learning
  • Focuses on the adaptation of the field from a theoretical point-of-view
  • Describes four major families of theoretical results in the literature
  • Summarizes existing results on adaptation in the field
  • Provides tips for future research
Ievgen Redko is an associate professor at INSA in Lyon since 2016. He obtained his PhD in computer Science, specialized in Data Science in 2015.Emilie Morvant is a Lecturer and a professor assistant at the Jean Monnet of Saint-Etienne University. She obtained her PhD in 2013 in Computer Science.Amaury Habrard is a full professor at the...
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Title:Domain Adaptation Theory: Available Theoretical ResultsFormat:HardcoverDimensions:250 pages, 9.41 × 7.24 × 0.98 inPublished:December 1, 2018Publisher:Elsevier ScienceLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:178548236X

ISBN - 13:9781785482366

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Table of Contents

1. Introduction.
2. State-of-the-art on statistical learning theory.
3. Domain adaptation problem.
4. Divergence based bounds.
5. PAC-Bayes bounds for domain adaptation.
6. Robustness and adaptation.
7. Stability and hypothesis transfer learning.
8. Impossibility results.
9. Conclusions and open discussions.