The Journal of Machine Learning Advances (JMLA) publishes high-quality original research on cutting-edge machine learning methods and their applications. We welcome contributions across three core areas:
Emerging paradigms such as meta-learning, self-supervised learning, continual learning, and active learning; learning theory including generalization, optimization, representation learning, and causal machine learning; as well as efficient learning techniques including model compression, distillation, pruning, and quantization.
Interpretability and transparency; fairness, debiasing, and algorithmic accountability; robustness and out-of-distribution generalization; privacy-preserving learning (differential privacy and federated learning); and the development of safe and responsible AI systems.
Applications that demonstrate methodological adaptation or innovation at the intersection of machine learning and artificial intelligence systems, including but not limited to:
We accept the following article types:
Original Research, Review, Mini Review, Short Communications, Commentary, Editorial, Research Highlight, Perspective.
Editor-in-Chief: Nianyin Zeng
Xiamen University, China
Frequency: Quarterly
Submission to First Decision: 22 days
Submission to Acceptance: 126 days
Accept to Publish: 30 days
Acceptance Rate: 25%
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