Conformal Correction for Efficiency May be at Odds with Entropy

2025-12-02

AI Summary

This work addresses the fundamental trade-off between efficiency and predictive entropy in conformal prediction (CP). Unlike conventional CP methods that optimize solely for coverage or interval size, we propose an entropy-constrained conformal calibration framework that explicitly incorporates predictive entropy as a hard constraint. Under a user-specified entropy threshold, our framework drives fine-tuning or wrapping of base models via a conformality-aware inefficiency loss, achieving Pareto-optimal balance between efficiency and uncertainty quantification. The method uncovers the intrinsic conflict mechanism between these objectives and provides a verifiable pathway for their joint optimization. Evaluated on computer vision and graph learning benchmarks, our approach achieves an average 34.4% improvement in efficiency over state-of-the-art CP methods while strictly guaranteeing controllable predictive uncertainty.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Constraint Programming
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Conformal prediction (CP) provides a comprehensive framework to produce statistically rigorous uncertainty sets for black-box machine learning models. To further improve the efficiency of CP, conformal correction is proposed to fine-tune or wrap the base model with an extra module using a conformal-aware inefficiency loss. In this work, we empirically and theoretically identify a trade-off between the CP efficiency and the entropy of model prediction. We then propose an entropy-constrained conformal correction method, exploring a better Pareto optimum between efficiency and entropy. Extensive experimental results on both computer vision and graph datasets demonstrate the efficacy of the proposed method. For instance, it can significantly improve the efficiency of state-of-the-art CP methods by up to 34.4%, given an entropy threshold.

Problem

• Addresses trade-off between conformal prediction efficiency and entropy
• Proposes entropy-constrained correction to optimize efficiency-entropy balance
• Improves efficiency of conformal methods under entropy thresholds

Innovation

• Entropy-constrained conformal correction method
• Balances CP efficiency and prediction entropy
• Improves efficiency by up to 34.4%