Information-Theoretic Methods for Trustworthy Machine Learning
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 54
Special Issue Editors
Interests: trustworthy machine learning; fairness and explainability; information theory; optimization; statistics; estimation theory; causal inference; coded computing
Interests: capacity of wireless networks; private/secure/coded/distributed storage/retrieval/computation; network coding; network information theory; quantum information theory
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning has advanced significantly in recent years, fundamentally revolutionizing decision-making in several applications. While these models excel at learning all patterns embedded in the data, blindly learning all patterns can have unintended consequences, raising concerns regarding security, privacy, fairness, explainability, robustness, and reliability. Thus, the following question must be answered: How can we design machine learning models responsibly to ensure trustworthy decision making?
Information-theoretic methods play a pivotal role in ensuring the trustworthiness of machine learning systems via the rigorous quantification and analysis of fundamental limits. Information-theoretic methods are useful in analyzing the flow of information within machine learning pipelines, identifying vulnerabilities and potential biases, developing strategies for privacy and security, and ensuring robustness under unreliability and adversaries. In essence, information-theoretic methods contribute to building machine learning systems that are transparent, accountable, and dependable, thereby fostering trust among users and stakeholders.
The Special Issue welcomes the submission of previously unpublished papers on information-theoretic methods for trustworthy machine learning. The scope of this Special Issue includes, but is not limited to, fairness, explainability, security, privacy, reliability, and robustness.
Dr. Sanghamitra Dutta
Prof. Dr. Syed A. Jafar
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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
- fairness
- explainability
- reliability
- privacy
- security
- robustness
