A Unified Framework for Human-Allied Learning of Probabilistic Circuits
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909433540378624 |
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| author | Karanam, Athresh Mathur, Saurabh Sidheekh, Sahil Natarajan, Sriraam |
| author_facet | Karanam, Athresh Mathur, Saurabh Sidheekh, Sahil Natarajan, Sriraam |
| contents | Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. Nevertheless, the existing body of research on PCs predominantly concentrates on data-driven parameter learning, often neglecting the potential of knowledge-intensive learning, a particular issue in data-scarce/knowledge-rich domains such as healthcare. To bridge this gap, we propose a novel unified framework that can systematically integrate diverse domain knowledge into the parameter learning process of PCs. Experiments on several benchmarks as well as real world datasets show that our proposed framework can both effectively and efficiently leverage domain knowledge to achieve superior performance compared to purely data-driven learning approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02413 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Unified Framework for Human-Allied Learning of Probabilistic Circuits Karanam, Athresh Mathur, Saurabh Sidheekh, Sahil Natarajan, Sriraam Machine Learning Artificial Intelligence Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions. Nevertheless, the existing body of research on PCs predominantly concentrates on data-driven parameter learning, often neglecting the potential of knowledge-intensive learning, a particular issue in data-scarce/knowledge-rich domains such as healthcare. To bridge this gap, we propose a novel unified framework that can systematically integrate diverse domain knowledge into the parameter learning process of PCs. Experiments on several benchmarks as well as real world datasets show that our proposed framework can both effectively and efficiently leverage domain knowledge to achieve superior performance compared to purely data-driven learning approaches. |
| title | A Unified Framework for Human-Allied Learning of Probabilistic Circuits |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.02413 |