Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimation
This provides a tool for uncertainty estimation in high-stakes applications of conditional generative models, but it is incremental as it builds on existing conformal prediction methods.
The paper tackles the problem of uncalibrated uncertainty in conditional generative models by proposing CP4Gen, a conformal prediction method using adaptive cluster-based density estimation, which achieves superior performance in prediction set volume and structural simplicity in experiments on synthetic and real-world datasets like climate emulation.
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.