ACM Transactions on Probabilistic Machine Learning
@ACM_TOPML
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ACM Transactions on Probabilistic Machine learning is the official account of @TheOfficialACM 's TOPML journal.
Joined December 2023
@ACM_TOPML has new Continuous Special Sections. https://t.co/gj01hCrcVC with topics being "Causal Inference and Reasoning", "Physics-informed Probabilistic Machine Learning" and "Uncertainty Quantification and Other Probabilistic Aspects in LLMs"
dl.acm.org
ACM Transactions on Probabilistic Machine Learning focuses on probabilistic methods that learn from data to improve performance on decision-making or prediction tasks under uncertainty. Optimization,...
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🎬 Hear from the Co-EiCs of @ACM_TOPML ! In this video, Wray Buntine, Fang Liu (@NotreDame), and Theodore Papamarkou share what makes the journal special and their vision for it. TOPML launched in 2023—and the second issue is now out! Watch now: https://t.co/9gP3K1YC0Q
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It is with great pleasure to inform you that the inaugural issue of TOPML is published now! For more information please visit the links below https://t.co/ymuqBMTSMX
https://t.co/qNqBVkX9AB
@ACMDL
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#CFP Special issue on Probabilistic programming The aim of this special issue is to present new approaches, techniques, tools, theories & experience reports about adopting, creating, applying & improving probabilistic programming For more info please visit https://t.co/2rZhMO6mIe
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#CFP Special Issue on Probabilistic Methods for Reliable Machine Learning in Open Environments, welcoming original contributions that utilize statistical & probabilistic methodologies to navigate complexities & align with human values. For more info see: https://t.co/gIzrzJFazu
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If you are interested in publishing on @TheOfficialACM official journal on #Probabilistic #MachineLearning TOPML is the one to apply! (refer to https://t.co/CN0hvYiPQ3 for Submission guidelines with Latex (preferred)/Word templates, which preparing and submitting your manuscript)
dl.acm.org
ACM Transactions on Probabilistic Machine Learning focuses on probabilistic methods that learn from data to improve performance on decision-making or prediction tasks under uncertainty. Optimization,...
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