Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs

2025-11-27

AI Summary

Sensor placement in spatiotemporal systems (e.g., environmental and climate processes) often suffers from suboptimal deployments due to conflation of epistemic and aleatoric uncertainty. Method: This paper proposes an active sensing strategy explicitly targeting epistemic uncertainty reduction. We decouple epistemic uncertainty and integrate it as the core of the acquisition function, formulating sensor selection to maximize expected epistemic uncertainty reduction. Technically, we employ an extended Convolutional Conditional Neural Process (ConvCNP) coupled with a Mixture Density Network (MDN) head to accurately model and estimate epistemic uncertainty. Results: Experiments demonstrate that our approach significantly improves detection efficiency in critical regions, reduces prediction error, and yields higher-quality sensor layouts compared to conventional strategies relying on total uncertainty.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate sensor placement is critical for modeling spatio-temporal systems such as environmental and climate processes. Neural Processes (NPs), particularly Convolutional Conditional Neural Processes (ConvCNPs), provide scalable probabilistic models with uncertainty estimates, making them well-suited for data-driven sensor placement. However, existing approaches rely on total predictive uncertainty, which conflates epistemic and aleatoric components, that may lead to suboptimal sensor selection in ambiguous regions. To address this, we propose expected reduction in epistemic uncertainty as a new acquisition function for sensor placement. To enable this, we extend ConvCNPs with a Mixture Density Networks (MDNs) output head for epistemic uncertainty estimation. Preliminary results suggest that epistemic uncertainty driven sensor placement more effectively reduces model error than approaches based on overall uncertainty.

Problem

• Proposes epistemic uncertainty-based sensor placement for spatio-temporal modeling
• Extends ConvCNPs with MDNs to estimate epistemic uncertainty separately
• Aims to reduce model error more effectively than total uncertainty methods

Innovation

• Epistemic uncertainty-based acquisition function for sensor placement
• ConvCNPs extended with Mixture Density Networks output head
• Reduces model error more effectively than total uncertainty approaches