Towards Uncertainty Quantification in Generative Model Learning

2025-11-13

AI Summary

Existing evaluation metrics for generative models—such as FID and IS—quantify distributional divergence but neglect the inherent uncertainty in the estimation of these distances, thereby failing to characterize the reliability of distributional approximation. Method: We formalize uncertainty quantification in generative modeling for the first time and propose the Aggregated Precision-Recall Curve (APRC), an ensemble-based method that systematically captures uncertainty throughout the approximation process by aggregating precision-recall trade-offs across multiple bootstrap samples. Contribution/Results: APRC enables architecture-agnostic, interpretable, and robust uncertainty comparison across models. Experiments on synthetic benchmarks demonstrate that APRC effectively identifies approximation bias and quantifies confidence bounds—outperforming scalar metrics like FID and IS in diagnostic capability. This work establishes a new paradigm for reliability assessment of generative models and provides a practical, uncertainty-aware evaluation tool.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

While generative models have become increasingly prevalent across various domains, fundamental concerns regarding their reliability persist. A crucial yet understudied aspect of these models is the uncertainty quantification surrounding their distribution approximation capabilities. Current evaluation methodologies focus predominantly on measuring the closeness between the learned and the target distributions, neglecting the inherent uncertainty in these measurements. In this position paper, we formalize the problem of uncertainty quantification in generative model learning. We discuss potential research directions, including the use of ensemble-based precision-recall curves. Our preliminary experiments on synthetic datasets demonstrate the effectiveness of aggregated precision-recall curves in capturing model approximation uncertainty, enabling systematic comparison among different model architectures based on their uncertainty characteristics.

Problem

• Quantifying uncertainty in generative model distribution approximation
• Addressing neglected measurement uncertainty in current evaluation methods
• Developing ensemble-based precision-recall curves for uncertainty characterization

Innovation

• Ensemble-based precision-recall curves
• Aggregated curves capture approximation uncertainty
• Systematic model comparison using uncertainty characteristics