Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimation

2026-01-29

AI Summary

Conditional generative models often lack reliable uncertainty quantification, hindering their trustworthy deployment in high-stakes applications. To address this limitation, this work proposes CP4Gen—a systematic conformal prediction framework tailored for conditional generative models. By leveraging adaptive clustering for density estimation, CP4Gen constructs prediction sets that are robust to outliers, structurally concise, and interpretable. The method effectively integrates conformal prediction with uncertainty quantification inherent to generative models. Empirical evaluations on synthetic data and climate simulation tasks demonstrate that CP4Gen significantly outperforms existing approaches, achieving valid prediction sets with substantially smaller volume and clearer structural coherence while maintaining rigorous coverage guarantees.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Conditional generative models map input variables to complex, high-dimensional distributions, enabling realistic sample generation in a diverse set of domains. A critical challenge with these models is the absence of calibrated uncertainty, which undermines trust in individual outputs for high-stakes applications. To address this issue, we propose a systematic conformal prediction approach tailored to conditional generative models, leveraging density estimation on model-generated samples. We introduce a novel method called CP4Gen, which utilizes clustering-based density estimation to construct prediction sets that are less sensitive to outliers, more interpretable, and of lower structural complexity than existing methods. Extensive experiments on synthetic datasets and real-world applications, including climate emulation tasks, demonstrate that CP4Gen consistently achieves superior performance in terms of prediction set volume and structural simplicity. Our approach offers practitioners a powerful tool for uncertainty estimation associated with conditional generative models, particularly in scenarios demanding rigorous and interpretable prediction sets.

Problem

• conformal prediction
• generative models
• uncertainty calibration
• conditional generation
• prediction sets

Innovation

• Conformal Prediction
• Generative Models
• Density Estimation
• Clustering
• Uncertainty Quantification