MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation
arXiv:2604.05446v2 Announce Type: replace-cross Abstract: Obtaining high-quality labels is costly, whereas unlabeled covariates are often abundant, motivating semi-supervised inference methods with reliable uncertainty quantification. Prediction-powered inference (PPI) leverages a machine-learning predictor trained on a small labeled sample to improve efficiency, but it can lose efficiency under model misspecification and suffer from coverage distortions due to label reuse. We introduce Machine-Learning-Assisted Generalized Entropy Calibration (MEC), a cross-fitted, calibration-weighted varian
The increasing reliance on machine learning across scientific and industrial applications is driving demand for more robust and reliable methods for uncertainty quantification and efficient data utilization.
This development offers a potential pathway to more accurate and efficient semi-supervised learning, enabling better decision-making in data-scarce or label-costly environments for strategic readers.
MEC introduces a new method to improve semi-supervised inference efficiency and reliability by addressing limitations of prior prediction-powered inference techniques, potentially mitigating model misspecification and label reuse issues.
- · AI/ML researchers
- · Data-intensive industries
- · Sectors with expensive data labeling
- · Healthcare and scientific research
- · Inefficient semi-supervised learning methods
- · Organizations over-reliant on fully supervised learning
Improved accuracy and reduced data labeling costs in applications utilizing semi-supervised learning.
Accelerated development and deployment of robust AI systems across various industries due to better data efficiency.
Enhanced trust and broader adoption of AI in critical domains where uncertainty quantification is paramount, potentially informing AI agent development.
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Read at arXiv cs.LG