Data-Efficient Prediction-Powered Calibration via Cross-Validation

2025-07-27

AI Summary

When calibration data are scarce, AI models struggle to reliably quantify predictive uncertainty. To address this, we propose a prediction-enabled calibration framework that jointly optimizes a synthetic label generator and estimates its systematic bias—without requiring additional ground-truth labels. Leveraging cross-validation, the method constructs bias-aware prediction intervals that guarantee strict statistical coverage. Crucially, label synthesis, bias estimation, and predictive calibration are unified within a single optimization objective, achieving superior trade-offs between data efficiency and calibration accuracy. Experiments on indoor localization demonstrate substantial improvements in calibration performance: under extreme sparsity (only 5–10 calibration samples), our approach maintains both high coverage probability and narrow interval width, outperforming existing methods in reliability and precision.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Calibration data are necessary to formally quantify the uncertainty of the decisions produced by an existing artificial intelligence (AI) model. To overcome the common issue of scarce calibration data, a promising approach is to employ synthetic labels produced by a (generally different) predictive model. However, fine-tuning the label-generating predictor on the inference task of interest, as well as estimating the residual bias of the synthetic labels, demand additional data, potentially exacerbating the calibration data scarcity problem. This paper introduces a novel approach that efficiently utilizes limited calibration data to simultaneously fine-tune a predictor and estimate the bias of the synthetic labels. The proposed method yields prediction sets with rigorous coverage guarantees for AI-generated decisions. Experimental results on an indoor localization problem validate the effectiveness and performance gains of our solution.

Problem

• Efficiently calibrate AI models with scarce data
• Fine-tune predictor and estimate synthetic label bias
• Ensure rigorous coverage for AI-generated decision sets

Innovation

• Uses cross-validation for efficient data utilization
• Simultaneously fine-tunes predictor and estimates bias
• Ensures rigorous coverage for AI-generated decisions