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Advances in Probabilistic 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: 9 March 2025 | Viewed by 393

Special Issue Editors


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Guest Editor
Department of Computer Science, Aalto University, 02150 Espoo, Finland
Interests: probabilistic machine learning; tractable probabilistic inference; Bayesian deep learning; Bayesian nonparametrics; probabilistic programming

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Guest Editor
Department of Information Systems, Decision Sciences and Statistics, ESSEC Business School, Singapore 139408, Singapore
Interests: statistical learning theory; mathematical statistics; Bayesian statistics; aggregation of estimators; approximate posterior inference
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Probabilistic modeling and reasoning are central to modern machine learning when dealing with all forms of uncertainties. In recent years, substantial progress has been made in the areas of approximate Bayesian inference, tractable probabilistic reasoning, and uncertainty quantification as a whole. Those advancements have resulted in, for example, a comeback of Bayesian deep learning and techniques that allow the effective and efficient quantification of uncertainties in complex scenarios. Moreover, the probabilistic approach has recently shown substantial potential in a wide range of application domains, including drug discovery and autonomous diving, and is a cornerstone for robust and reliable machine learning.

This Special Issue aims to provide a platform for the presentation of advancements in the field of probabilistic machine learning and Bayesian inference with a particular emphasis on computational approaches for large-scale problems. In particular, we invite submissions presenting theoretical and methodological advancements in the field, as well as application papers. Possible topics include, but are not limited to, advancements in approximate Bayesian inference (e.g., variational inference, parallel tempering), tractable probabilistic modeling (e.g., probabilistic circuits), and applications in Bayesian deep learning (e.g., uncertainty quantification in LLMs), as well as other challenging large-scale scenarios.

Dr. Martin Trapp
Prof. Dr. Pierre Alquier
Guest Editor

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

  • probabilistic machine learning
  • approximate Bayesian inference
  • variational inference
  • tractable probabilistic inference
  • Bayesian deep learning
  • probabilistic circuits
  • uncertainty quantification

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Published Papers

This special issue is now open for submission.
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