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
![Journal of Machine Learning](https://global-sci.com/images/journals/JMLv3i1.jpg?w=900&h=&fit=&c=2&assets_cache=1729244731)
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
Mathematics Department, Duke University
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
Mathematics Department, Duke University
JML's Editor-in-Chief Weinan E was awarded 2023 ICIAM Maxwell Prize. Congratulations!
JML's editorial board is formed in October 2021, and it will begin to publish papers in March, 2022.
![Bridging Traditional and Machine Learning-Based Algorithms for Solving PDEs: The Random Feature Method](https://global-sci.com/images/articles/87611.png?w=100&h=&fit=&c=2&assets_cache=20241217060950&bg=white)
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
![On the Existence of Optimal Shallow Feedforward Networks with ReLU Activation](https://global-sci.com/images/articles/91117.png?w=100&h=&fit=&c=2&assets_cache=20241218060940&bg=white)
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
![Prompt Engineering Through the Lens of Optimal Control](https://global-sci.com/images/articles/87603.png?w=100&h=&fit=&c=2&assets_cache=20241218060939&bg=white)
Journal Article
Prompt Engineering Through the Lens of Optimal Control
Journal of Machine Learning, Vol. 2 (2023), Iss. 4 : pp. 241–258
![Embedding Principle: A Hierarchical Structure of Loss Landscape of Deep Neural Networks](https://global-sci.com/images/articles/87607.png?w=100&h=&fit=&c=2&assets_cache=20241218060938&bg=white)
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
![Beyond the Quadratic Approximation: The Multiscale Structure of Neural Network Loss Landscapes](https://global-sci.com/images/articles/87610.png?w=100&h=&fit=&c=2&assets_cache=20241217060949&bg=white)
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
![Reinforcement Learning Algorithm for Mixed Mean Field Control Games](https://global-sci.com/images/articles/87598.png?w=100&h=&fit=&c=2&assets_cache=20241214060555&bg=white)
Journal Article
Reinforcement Learning Algorithm for Mixed Mean Field Control Games
Journal of Machine Learning, Vol. 2 (2023), Iss. 2 : pp. 108–137
![Ab-Initio Study of Interacting Fermions at Finite Temperature with Neural Canonical Transformation](https://global-sci.com/images/articles/87606.png?w=100&h=&fit=&c=2&assets_cache=20241216060953&bg=white)
Journal Article
Ab-Initio Study of Interacting Fermions at Finite Temperature with Neural Canonical Transformation
Journal of Machine Learning, Vol. 1 (2022), Iss. 1 : pp. 38–59
![DeePN$^2$: A Deep Learning-Based Non-Newtonian Hydrodynamic Model](https://global-sci.com/images/articles/87608.png?w=100&h=&fit=&c=2&assets_cache=20241218060938&bg=white)
Journal Article
DeePN$^2$: A Deep Learning-Based Non-Newtonian Hydrodynamic Model
Journal of Machine Learning, Vol. 1 (2022), Iss. 1 : pp. 114–140
![Bridging Traditional and Machine Learning-Based Algorithms for Solving PDEs: The Random Feature Method](https://global-sci.com/images/articles/87611.png?w=100&h=&fit=&c=2&assets_cache=20241217060950&bg=white)
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
![The Cost-Accuracy Trade-Off in Operator Learning with Neural Networks](https://global-sci.com/images/articles/87612.png?w=100&h=&fit=&c=2&assets_cache=20241216060953&bg=white)
Journal Article
The Cost-Accuracy Trade-Off in Operator Learning with Neural Networks
Journal of Machine Learning, Vol. 1 (2022), Iss. 3 : pp. 299–341