Advances in Statistical Machine Learning: Interpretability, Explainability and High-Performance Algorithms

Dear Colleagues,

Machine learning is transforming numerous scientific disciplines, yet its practical adoption in high-stakes domains often depends on the interpretability and explainability of predictive models. This Special Issue aims to explore cutting-edge developments in statistical machine learning, with a particular focus on enhancing the transparency, robustness, and efficiency of algorithms. We invite both theoretical advancements and innovative applications, with the latter requiring a strong justification for their originality and impact.

The key topics of interest include, but are not limited to, the following:

• Statistical approaches in machine learning: Novel frameworks that integrate statistical learning principles to improve generalization, stability, and uncertainty quantification.
• Interpretability and explainability: The development of new methodologies to make machine learning models more interpretable, particularly for black box models.
• High-performance algorithms: Enhancements in the efficiency, scalability, and accuracy of learning models.
• Integration of large language models (LLMs) in predictive tasks: Methodological and theoretical contributions to the application of LLMs in forecasting, classification, and decision making.
• Textual analysis and machine learning: Applications of machine learning in natural language processing with a strong emphasis on statistical modeling.
• Causal machine learning: Methods that go beyond correlation-based learning, incorporating causality into machine learning frameworks.
• Application-driven machine learning: Highly innovative applications with strong theoretical justifications and real-world significance.

Prof. Dr. Fabrizio Maturo
Dr. Elvira Romano
Guest Editors

Manuscript Submission Information

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