arXiv:2507.02215v2 Announce Type: replace-cross Abstract: Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with heavily polluted data. Existing methods that are effective in the small-noise regime are suboptimal when large noise is present. To address this issue, we propose a hybrid approach that combines Christoffel sampling with optimal experimental design. We show that the proposed algorithm enjoys appropriate optimality properties for both sample point generation and noise mollification, leading to improv
This paper addresses a fundamental challenge in machine learning — learning from highly noisy data — which is increasingly prevalent as data sources diversify and real-world sensing becomes more complex.
Improved methods for handling noisy data will enable more robust and reliable AI systems, especially in applications where data quality cannot be perfectly controlled, impacting fields from scientific research to industrial automation.
The ability to efficiently estimate conditional expectations from heavily polluted data will lead to more accurate models and reduce the need for extensive data cleaning or high-fidelity sensor systems.
- · AI/ML researchers
- · Industries with noisy data (e.g., manufacturing, IoT, life sciences)
- · Developers of robust AI systems
- · Traditional data cleaning services
- · Systems highly reliant on pristine data inputs
More accurate and reliable AI models will emerge in domains previously hindered by data quality issues.
This could accelerate AI adoption in real-world environments where data is inherently messy and unpredictable.
Reduced data preprocessing overhead might free up compute and human resources for more advanced model development and deployment.
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Read at arXiv cs.LG