Academic Journal
Q1Foundations and Trends in Machine Learning
About Foundations and Trends in Machine Learning
Foundations and Trends in Machine Learning is a scholarly journal published by an academic publisher (United States).
Key facts: ISSN 1935-8237, 1935-8245; H-index 52; Research areas: Reinforcement Learning in Robotics; Gaussian Processes and Bayesian Inference; Neural Networks and Applications; Machine Learning and Algorithms.
Source-backed journal facts
Topics in published research
Reinforcement Learning in Robotics; Gaussian Processes and Bayesian Inference; Neural Networks and Applications; Machine Learning and Algorithms; Bayesian Methods and Mixture Models; Advanced Graph Neural Networks.
OpenAlex classifies topics from published works. These topics are not the publisher’s official aims and scope.
Source: OpenAlex source record. Retrieved 2026-10-03. Source record updated 2026-10-02. OpenAlex metrics are different from SCImago metrics and the Clarivate Journal Impact Factor.
Journal Metrics
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Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
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2025-01-23 · DOI: 10.1561/2200000112Hyperparameter Optimization in Machine Learning
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2025 · DOI: 10.1561/2200000088An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes
George H. Chen
2024-12-12 · DOI: 10.1561/2200000114Automated Deep Learning: Neural Architecture Search Is Not the End
Xuanyi Dong, David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys et al.
2024-02-27 · DOI: 10.1561/2200000119AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys
2024-02-21 · DOI: 10.1561/2200000093Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning
Drago Plečko, Elias Bareinboim
2024-01-31 · DOI: 10.1561/2200000106User-friendly Introduction to PAC-Bayes Bounds
Pierre Alquier
2024-01-22 · DOI: 10.1561/2200000100A Friendly Tutorial on Mean-Field Spin Glass Techniques for Non-Physicists
Andrea Montanari, Subhabrata Sen
2024-01-10 · DOI: 10.1561/2200000105Reinforcement Learning, Bit by Bit
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi et al.
2023-07-11 · DOI: 10.1561/2200000097Tutorial on Amortized Optimization
Brandon Amos
2023-06-27 · DOI: 10.1561/2200000102Conformal Prediction: A Gentle Introduction
Anastasios N. Angelopoulos, Stephen Bates
2023-03-27 · DOI: 10.1561/2200000101Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats
Nicolas Guigui, Nina Miolane, Xavier Pennec
2023-02-22 · DOI: 10.1561/2200000098Graph Neural Networks for Natural Language Processing: A Survey
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo et al.
2023-01-25 · DOI: 10.1561/2200000096Model-based Reinforcement Learning: A Survey
Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker et al.
2023-01-04 · DOI: 10.1561/2200000086Divided Differences, Falling Factorials, and Discrete Splines
Ryan J. Tibshirani
2022-07-21 · DOI: 10.1561/2200000099Reviews
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Version History
September 25, 2026 at 6:50 am
January 9, 2025