Foundations and Trends in Machine Learning

Aims & Scope

The growth in all aspects of research in the last decade has led to a multitude of new publications and an exponential increase in published research.

Finding a way through the excellent existing literature and keeping up to date has become a major time-consuming problem.

Electronic publishing has given researchers instant access to more articles than ever before.

But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic?

To address this problem Foundations and Trends® in Machine Learning publishes high-quality survey and tutorial monographs of the field.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	25.4
2024	65.30

Journal Rank

Year	Value
2024	10

Journal Citation Indicator

Year	Value
2024	1613

SJR (SCImago Journal Rank)

Year	Value
2024	22.797

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	42

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

  Citation: 13388

  Authors: Stephen

• Learning Deep Architectures for AI

  Citation: 5764

  Authors: Y.

• Advances and Open Problems in Federated Learning

  Citation: 2880

  Authors: Peter, H. Brendan, Brendan, Aurélien, Mehdi, Arjun, Kallista, Zachary, Graham, Rachel, Rafael G. L., Hubert, Salim, David, Josh, Zachary, Adrià, Badih, Phillip B., Marco, Zaid, Chaoyang, Lie, Zhouyuan, Ben, Justin, Martin, Tara, Gauri, Mikhail, Jakub, Aleksandra, Farinaz, Sanmi, Tancrède, Yang, Prateek, Mehryar, Richard, Ayfer, Rasmus, Hang, Daniel, Ramesh, Mariana, Dawn, Weikang, Sebastian U., Ziteng, Ananda Theertha, Florian, Praneeth, Jianyu, Li, Zheng, Qiang, Felix X., Han, Sen

• An Introduction to Variational Autoencoders

  Citation: 1684

  Authors: Diederik P., Max

• Computational Optimal Transport: With Applications to Data Science

  Citation: 1521

  Authors: Gabriel, Marco

• Graphical Models, Exponential Families, and Variational Inference

  Citation: 1291

  Authors: Martin J., Michael I.

• Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems

  Citation: 941

  Authors: Sébastien

• Online Learning and Online Convex Optimization

  Citation: 925

  Authors: Shai

• An Introduction to Deep Reinforcement Learning

  Citation: 898

  Authors: Vincent, Peter, Riashat, Marc G., Joelle

• Convex Optimization: Algorithms and Complexity

  Citation: 758

  Authors: Sébastien

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