arXiv:2504.15388v3 Announce Type: replace-cross Abstract: In the context of multivariate nonparametric regression with missing covariates, we propose Pattern Embedded Neural Networks (PENNs), which can be applied in conjunction with any existing imputation technique. In addition to a neural network trained on the imputed data, PENNs pass the vectors of observation indicators through a second neural network to provide a compact representation. The outputs are then combined in a third neural network to produce final predictions. Our main theoretical result exploits an assumption that the observa
The increasing sophistication of AI models and their application to real-world datasets often fraught with imperfections makes robust missing data handling crucial.
This research directly addresses a fundamental challenge in applying deep learning, potentially improving model reliability and the utility of large, incomplete datasets across various industries.
The proposed PENN architecture offers a conceptually simple yet powerful method to integrate missing data patterns directly into neural networks, potentially leading to more accurate predictions in scenarios with incomplete information.
- · AI/ML researchers
- · Data scientists
- · Industries with complex, incomplete datasets (e.g., healthcare, finance)
- · Cloud AI platform providers
- · Traditional simpler imputation methods
- · Organizations relying solely on complete datasets
Improved performance and reliability of deep learning models operating on real-world, incomplete data.
Accelerated development and deployment of AI solutions in data-rich but messy environments.
Reduced data pre-processing overhead, allowing for faster iteration cycles in AI development and research.
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Read at arXiv cs.LG