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The Association for Computing Machinery (ACM) has unveiled the inaugural issue of ACM Transactions on Probabilistic Machine Learning (TOPML), marking a significant addition to the field of machine learning research. This new Gold Open Access journal aims to provide a platform for high-quality research on probabilistic methods that enhance decision-making and prediction tasks under uncertainty.

As part of ACM's ongoing commitment to becoming a fully Open Access publisher by 2025, TOPML will be freely accessible through the ACM Digital Library, allowing researchers to read and share the published papers without restrictions.

Probabilistic machine learning has become a cornerstone of artificial intelligence (AI) advancements. By leveraging probability models, which mathematically describe all possible outcomes of an experiment, researchers have significantly improved machine learning algorithms. This journal will explore the future potential of these techniques, which remain a crucial part of AI’s evolution.

TOPML aims to cover a broad scope of probabilistic machine learning research, including theoretical foundations, novel algorithms, and applications across various disciplines. The journal focuses on the integration of methodology with real-world examples and interdisciplinary collaboration. Topics of interest include theoretical contributions, methodological approaches supported by empirical evidence, and multidisciplinary applications that leverage probabilistic methods.

The editorial leadership of the journal consists of Co-Editors-in-Chief Wray Buntine (VinUniversity, Vietnam), Fang Liu (University of Notre Dame, USA), and Theodore Papamarkou (Zhejiang Normal University, China), supported by a global team of experts. As highlighted in their inaugural editorial, the launch of TOPML comes at a time of rapid growth in machine learning research, underscoring the journal’s potential to drive future discoveries and address ethical considerations, including data privacy and fairness.