Journal of Machine Learning (JML) publishes high quality research papers in all areas of machine learning, including innovative algorithms of machine learning, theories of machine learning, important applications of machine learning in AI, natural sciences, social sciences, and engineering etc. The journal emphasizes a balanced coverage of both theory and practice. The journal is published in a timely fashion in electronic form. All articles in JML are open-access and
there is no charge for the authors.
Journal of Machine Learning
Managing Editors
E, Weinan; Lu, Jianfeng; John Xu, Zhi-Qin
Impact Factor:
5-Year Impact Factor:
Aims and Scope:
Journal of Machine Learning (JML) publishes high quality research papers in all areas of machine learning, including innovative algorithms of machine learning, theories of machine learning, important applications of machine learning in AI, natural sciences, social sciences, and engineering etc. The journal emphasizes a balanced coverage of both theory and practice. The journal is published in a timely fashion in electronic form. All articles in JML are open-access and there is no charge for the authors.
MathSciNet
Journal Details
Publishing since: 2022
Number of Volumes: 3
Number of Issues: 9
ISSN (Print): 2790-203X
Electronic: 2790-2048
Managing Editors: E, Weinan; Lu, Jianfeng; John Xu, Zhi-Qin
Impact Factor:
CiteScore:
Subjects: Machine learning
Language:
Description
Jianfeng Lu
Mathematics Department, Duke University
Arnulf Jentzen
ajentzen@uni-muenster.de
School of Data Science and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen & Institute for Analysis and Numerics, University of Münster
Jianfeng Lu
Mathematics Department, Duke University
JML's Editor-in-Chief Weinan E was awarded 2023 ICIAM Maxwell Prize. Congratulations!
2022-11-09
JML's editorial board is formed in October 2021, and it will begin to publish papers in March, 2022.
2021-12-18
Journal Article
Bridging Traditional and Machine Learning-Based Algorithms for Solving PDEs: The Random Feature Method
Journal of Machine Learning, Vol. 1 (2022), Iss. 3 : pp. 268–298
Journal Article
On the Existence of Optimal Shallow Feedforward Networks with ReLU Activation
Journal of Machine Learning, Vol. 3 (2024), Iss. 1 : pp. 1–22
Journal Article
Beyond the Quadratic Approximation: The Multiscale Structure of Neural Network Loss Landscapes
Journal of Machine Learning, Vol. 1 (2022), Iss. 3 : pp. 247–267
Journal Article
Embedding Principle: A Hierarchical Structure of Loss Landscape of Deep Neural Networks
Journal of Machine Learning, Vol. 1 (2022), Iss. 1 : pp. 60–113
Journal Article
Prompt Engineering Through the Lens of Optimal Control
Journal of Machine Learning, Vol. 2 (2023), Iss. 4 : pp. 241–258