Representation Learning: Theory, Applications and Ethical Issues II
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6924
Special Issue Editors
Interests: kernel methods; preference learning; recommender systems; multiple kernel learning; interpretable machine learning; deep neural networks
Special Issues, Collections and Topics in MDPI journals
Interests: recommender systems; kernel methods; interpretable machine learning; security/privacy in machine learning; deep neural networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The representation problem has always been at the core of machine learning. Finding good data representation is the common denominator of many machine learning subtopics, such as feature selection, kernel learning, and deep learning. The recent rise of deep learning technologies has opened up new and fascinating possibilities for researchers in many fields. However, deep networks often fall short when it comes to being interpreted or explained. Hence, in addition to the effectiveness of a representation, there is the need to face many related problems, for example, interpretability, robustness, and fairness.
Information theory has shown great potential to solve many of the above-mentioned issues: (i) design robust loss functions for neural network training (e.g., Minimum Error Entropy Criterion); (ii) Information theoretic principles, such as the Information Bottleneck, to explain the generalization of DNNs; (iii) interpretation and explanation of DNNs through information-theoretic techniques; (iv) information-theoretic framework for designing fair predictors from data.
The purpose of this Special Issue is to highlight the state-of-the-art of the representation of learning both from a theoretical and a practical perspective, which has received a lot of attention from the research community. We particularly welcome work on information theory for deep and machine learning. In 2022, we will continue this trend with a 2nd volume. Possible topics include but are not limited to the following:
- Deep and shallow representation learning.
- Generative and adversarial representation learning.
- Robust representations for security.
- Representation learning for fair and ethical learning.
- Representation learning for interpretable machine learning.
- Representation learning under privacy constraints, e.g., federated learning.
- Representation learning in other domains, e.g., recommender systems, natural language processing, cybersecurity, process mining.
- Information-theoretic principles for the generalization and robustness of deep neural networks.
- Interpretation/explanation of deep neural networks with information-theoretic methods.
- Information-theoretic methods in generative models, causal representation learning and reinforcement learning
To see the first volume of the Special Issue, please see:
https://www.mdpi.com/journal/entropy/special_issues/Representation_Learning
Dr. Fabio Aiolli
Dr. Mirko Polato
Guest Editors
Manuscript Submission Information
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Keywords
- representation learning
- deep learning
- interpretability
- explainability
- fairness
- security & privacy
- information theory
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