Managing Uncertainty in Regression Neural Networks - From Prediction Intervals to Adaptive Sampling

📚 Understanding and managing uncertainty is a critical aspect of deploying regression neural network models in real-world scientific and engineering applications. This presentation introduces two novel contributions aimed at improving uncertainty quantification and guiding data acquisition under uncertainty. The first is DualAQD, a dual-network architecture for generating high-quality prediction intervals (PIs). DualAQD integrates a custom loss function that minimizes interval width while ensuring coverage constraints, striking a balance between tightness and reliability of uncertainty estimates. It consistently outperforms existing PI-generation techniques in both interval efficiency and prediction accuracy across diverse datasets. Building on DualAQD uncertainty modeling, we present ASPINN, an adaptive sampling strategy designed for data-scarce environments where measurement collection is costly or constrained. ASPINN addresses this by focusing on epistemic uncertainty reduction in regression problems, using NN-generated PIs to guide adaptive data acquisition to strategically select new data points that most reduce model uncertainty. By incorporating a Gaussian Process surrogate to support batch sampling, ASPINN balances informativeness and diversity in acquisition decisions. Empirical evaluations show that ASPINN achieves faster convergence and greater uncertainty reduction compared to leading alternatives. Together, these methods offer a robust framework for uncertainty-aware learning in regression tasks.

Jun 26, 2025 2:45 PM — 3:30 PM