Neural Conjugate Aggregation: Identifiable Unsupervised Multi-Sensor Regression under Heterogeneous Sensor Bias
Read the original on arXiv AI →The paper introduces Neural Conjugate Aggregation (NCAM), a hierarchical Bayesian model that fuses multiple noisy, biased sensor measurements without ground‑truth labels. NCAM learns sensor‑specific bias and reliability conditioned on context, producing an analytically tractable posterior over a latent target with decomposed epistemic and aleatoric uncertainty. The method resolves non‑identifiability via sensor anchoring and variance regularization, and adds locally adaptive Monte Carlo conformal prediction to provide heteroscedastic prediction intervals with coverage guarantees. Experiments on synthetic and real‑world air‑quality data show that NCAM outperforms unsupervised baselines such as mean aggregation, probabilistic PCA, and Kalman filtering in predictive accuracy and uncertainty calibration.
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