arXiv:2410.06329v4 Announce Type: replace-cross Abstract: Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant objective in statistical signal processing and machine learning. Modelling the joint PMF as a tensor that admits a low-rank canonical polyadic decomposition (CPD) has enabled the development of efficient PMF estimation algorithms. However, these algorithms require the rank (model order) of the tensor to be specified beforehand. In real-world applications, the true rank is unknown. Therefore, an appr
The continuous advancements in machine learning and statistical signal processing are driving the need for more robust and autonomous data analysis techniques, particularly for complex probabilistic models.
This development addresses a fundamental challenge in AI and statistical learning by enabling more accurate and automatic estimation of joint probability distributions without prior knowledge of model structure, reducing human intervention and error.
The ability to automatically determine model order in low-rank tensor decomposition for PMF estimation improves the autonomy and reliability of AI systems that rely on probabilistic inference.
- · AI/ML researchers
- · Data scientists
- · Developers of autonomous AI agents
- · Statistical signal processing applications
- · Manual model selection processes
- · Systems highly dependent on expert-driven parameter tuning
Improved accuracy and efficiency in probabilistic modeling for AI systems.
Faster development and deployment cycles for AI applications that leverage complex statistical inference.
Enhanced capabilities for AI agents to understand and predict complex systems with less human oversight.
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