Learning U-Statistics with Active Inference
This work develops an active inference framework for U-statistics to efficiently estimate parameters using a limited budget for acquiring labels. It uses an augmented inverse probability weighting U-statistic that incorporates machine learning predictions and optimal sampling rules derived from minimizing variance. The method achieves significant estimation efficiency gains while maintaining valid statistical inference under a fixed labeling constraint.