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About Foundations and Trends in Machine Learning

Foundations and Trends® welcomes monographs that touch on fundamental problems in machine learning from theoretical, methodological, and/or computational perspectives. We are particularly interested in monographs that seek to bridge such problems and perspectives with those from related fields, including (but not limited to) statistics, economics, and optimization.

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Hyperparameter optimization in machine learning
Artificial intelligence for science in quantum, atomistic, and continuum systems
Continual Learning as Computationally Constrained Reinforcement Learning
A Tutorial on Meta-Reinforcement Learning
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
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Indexed in:

  • ACM Guide, 
  • Cabell's International, 
  • Computing Reviews, 
  • DBLP, 
  • EI Compendex, 
  • Electronic Journals Library, 
  • Emerging Sources Citation Index (ESCI), 
  • Google Scholar, 
  • INSPEC, 
  • PubGet, 
  • SCOPUS, 
  • Ulrich's, 
  • Zentralblatt Math 

Editorial information

Editor-in-Chief: Ryan Tibshirani, University of California Berkeley

 

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