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

TL;DR

This paper introduces CP4Gen, a conformal prediction method for conditional generative models that uses clustering-based density estimation to produce calibrated, interpretable, and less complex uncertainty sets, improving trustworthiness in high-stakes applications.

Contribution

The paper presents CP4Gen, a novel conformal prediction approach that enhances uncertainty quantification for generative models through adaptive clustering-based density estimation.

Findings

CP4Gen outperforms existing methods in prediction set volume.

It produces more interpretable and structurally simple prediction sets.

Experimental results include synthetic and climate emulation datasets.

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.