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

• URL: http://arxiv.org/abs/2601.22298v1
• Date: Thu, 29 Jan 2026 20:23:33 GMT
• Title: Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimation
• Abstract summary: Conditional generative models map input variables to complex, high-dimensional distributions, enabling realistic sample generation in a diverse set of domains.<n>A critical challenge with these models is the absence of calibrated uncertainty, which undermines trust in individual outputs for high-stakes applications.<n>We propose a systematic conformal prediction approach tailored to conditional generative models, leveraging density estimation on model-generated samples.
• Score: 9.400964144779937
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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