YOUR GO-TO INTELLIGENCE RESOURCE FOR THE SCHOLARLY PUBLISHING INDUSTRY

ACM, the Association for Computing Machinery, has announced the launch of a new Gold Open Access journal, ACM Transactions on Probabilistic Machine Learning (TOPML). The journal is now accepting submissions from researchers in the field of probabilistic machine learning.

TOPML aims to publish research articles that focus on probabilistic methods that learn from data to enhance decision-making or prediction tasks under uncertainty. The journal welcomes contributions that explore optimization, decision-theoretic, or information-theoretic methods as long as they are supported by a probabilistic structure. The scope of TOPML covers topics such as statistical inference, uncertainty quantification, predictive calibration, data generation and sampling, causal inference, stability, and scalability.

Researchers are encouraged to submit their work to ACM Transactions on Probabilistic Machine Learning in areas including theoretical, methodological, and applied contributions. The journal welcomes theoretical contributions that introduce novel methodology, as well as methodological and applied contributions that propose probabilistic approaches supported by empirical evidence from non-trivial examples or applications. Additionally, TOPML is open to multidisciplinary approaches that incorporate a probabilistic structure.

Co-Editor-in-Chief of TOPML, Professor Wray Buntine from the VinUniversity in Hanoi, Vietnam, expressed the importance of probabilistic modeling in machine learning and the need to advance the field through knowledge sharing. Buntine, who previously led the Machine Learning Group at Monash University in Melbourne, Australia, believes that the new ACM journal will provide an ideal platform for researchers to share ideas and contribute to the improvement of decision-making tasks.

TOPML's Co-Editor-in-Chief, Professor Fang Liu from the University of Notre Dame in Indiana, highlighted the goal of the journal to attract contributions that address ethical considerations in probabilistic machine learning, such as data privacy and fairness. Liu, an elected fellow of the American Statistical Association, believes that these topics are receiving significant attention in the field.

Theodore Papamarkou, Co-Editor-in-Chief of TOPML and Reader in the Mathematics of Data Science at the University of Manchester in the UK, emphasized the importance of global collaboration in addressing research questions in high-dimensional settings. Papamarkou believes that working together with colleagues from around the world will enable the quantification of uncertainty and interpretation in deep learning and other contemporary machine learning models.

The first issue of ACM Transactions on Probabilistic Machine Learning is scheduled for publication in 2024. The journal's editorial team comprises the three Co-Editors-in-Chief, six Senior Advisory Editors, a Social Media Editor, 62 Senior Associate Editors, and 111 Editorial Board Members from various countries, reflecting ACM's global membership and the diversity of the field.

Researchers in the field of probabilistic machine learning are encouraged to submit their manuscripts to TOPML to contribute to the advancement of the discipline and promote the sharing of knowledge and ideas among the scientific community.