Information-Theoretic Approaches for Machine Learning and AI
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 10 December 2025 | Viewed by 642
Special Issue Editors
Interests: coded distributed computation; privacy-preserving and trustworthy machine learning; blockchain security and scalability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of artificial intelligence (AI) technology, especially large language models, the ways in which information is acquired, processed, and transmitted are undergoing revolutionary changes. In this context, Shannon entropy and information theory, as fundamental theories for understanding and measuring information, play a crucial role.
As the complexity of deep learning models continues to increase, their internal mechanisms often become a “black box”, posing challenges to the credibility and application of these models. By introducing methods from information theory, we can explore how to quantify the uncertainty and information flow within models, thereby revealing their decision-making processes. This not only aids in understanding the internal workings of the models but also provides effective guidance for model optimization and downstream tasks, such as multimodal compression and knowledge editing. Simultaneously, quantum entropy and quantum information theory offer entirely new perspectives and tools, which are expected to propel the forefront of AI in computational capabilities, algorithm design, and secure communication. Coding theory also plays a critical role in machine learning, by improving the efficiency, privacy, and security of data processing through information encoding and error correction.
The aim of this Special Issue is to attract research investigations, from an information–theoretic perspective, addressing current challenges faced by theory and applications of machine learning. Prospective authors are invited to submit original research contributions on leveraging information theory and quantum information theory, in solving problems on (but not limited to) the following topics:
- Model interpretability;
- Reinforcement learning;
- Data compression and semantic communication;
- Federated learning;
- Large language models;
- Optimization;
- Sustainable AI;
- Security and privacy;
- Unbiasedness and fairness in AI.
Prof. Dr. Songze Li
Prof. Dr. Linqi Song
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- information theory
- coding theory
- data compression
- quantum computing
- semantic information theory
- statistical learning theory
- reinforcement learning
- large language models
- federated learning
- security and privacy
- unbiasedness and fairness
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