Neural Conjugate Aggregation: Identifiable Unsupervised Multi-Sensor Regression under Heterogeneous Sensor Bias

• URL: http://arxiv.org/abs/2606.22200v1
• Date: Sat, 20 Jun 2026 19:30:21 GMT
• Title: Neural Conjugate Aggregation: Identifiable Unsupervised Multi-Sensor Regression under Heterogeneous Sensor Bias
• Abstract summary: We study regression-based data fusion under uncertainty, where multiple noisy and biased measurement sources are available but ground-truth labels are absent during training.<n>This setting arises in sensor networks, simulation ensembles, and scientific monitoring systems where supervision is costly or infeasible.<n>We propose the Neural Conjugate Aggregation Model (NCAM), a hierarchical Bayesian framework that combines neural networks with conjugate Gaussian inference for unsupervised multi-source fusion.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study regression-based data fusion under uncertainty, where multiple noisy and biased measurement sources are available but ground-truth labels are absent during training. This setting arises in sensor networks, simulation ensembles, and scientific monitoring systems where supervision is costly or infeasible. We propose the Neural Conjugate Aggregation Model (NCAM), a hierarchical Bayesian framework that combines neural networks with conjugate Gaussian inference for unsupervised multi-source fusion. NCAM learns source-specific bias and reliability conditioned on contextual covariates, yielding an analytically tractable posterior over a latent target variable with decomposed epistemic and aleatoric uncertainty. Structural non-identifiability is resolved through sensor anchoring and variance regularization, enabling stable and interpretable posterior aggregation. To complement Bayesian uncertainty with finite-sample guarantees, we integrate locally adaptive Monte Carlo conformal prediction, producing heteroscedastic prediction intervals with coverage guarantees under exchangeability assumptions. Experiments on synthetic and real-world air-quality datasets demonstrate improved predictive accuracy and well-calibrated uncertainty compared to unsupervised baselines, including mean aggregation, probabilistic PCA, and Kalman filtering.

Related papers

• Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation [71.74203645336385]
  Impute-EM alternates between imputing missing entries with the current model and refitting a diffusion backbone on completed data.<n>We characterize the update and show that the observed mask-indexed marginals match the targets at the limit.
  arXiv  Detail & Related papers  (2026-09-14T09:39:13Z)
• Multi-Source Complex Network Reconstruction via Wasserstein Distributionally Robust Optimization and Algorithm Unrolling [9.526111877241602]
  We propose MS-WDRO, a graph learning framework that fuses heterogeneous sources via their weighted Wasserstein barycenter.<n>Minimizing worst-case risk yields a tractable regularized Laplacian estimator solved efficiently via a provably convergent ADMM scheme.<n>MS-WDRO consistently outperforms seven baselines in graph recovery, sample efficiency, and downstream diagnostic utility.
  arXiv  Detail & Related papers  (2026-08-20T11:30:59Z)
• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• Generalized Neural Distributional Regression [0.0]
  We introduce the Generalized Neural Distributional Regression (GNDR) framework, which seamlessly embeds deep neural networks into the space of classical probability distributions.<n>We demonstrate the framework's versatility and superior distributional calibration across diverse modalities, including overdispersed clinical counts, right-ed transcriptomic survival profiles under a cure, and zero-truncated age distributions from unstructured facial images.
  arXiv  Detail & Related papers  (2026-06-16T19:01:57Z)
• A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting [3.68407754316989]
  This paper applies the law of total variance to the joint setting of neural network regression and Bayesian posterior approximation.<n>A wind power-specific evaluation framework is proposed to validate disentanglement without access to ground-truth uncertainty labels.<n>Across synthetic and real-world experiments, the decomposed AU and EU components respond in theoretically consistent directions.
  arXiv  Detail & Related papers  (2026-05-21T12:23:14Z)
• Local Conformal Calibration of Dynamics Uncertainty from Semantic Images [11.10792616622245]
  We introduce Observation-aware Conformal Uncertainty Local-Calibration (OCULAR)<n>OCULAR is a conformal prediction-based algorithm that uses perception information to provide uncertainty quantification guarantees.<n>We validate our algorithm on a double-integrator system subject to random perturbations and significant model mismatch.
  arXiv  Detail & Related papers  (2026-05-13T05:33:42Z)
• Conformal Prediction for Multi-Source Detection on a Network [59.17729745907474]
  We study the multi-source detection problem.<n>Given snapshot observations of node infection status on a graph, estimate the set of source nodes that initiated the propagation.<n>We propose a novel conformal prediction framework that provides statistically valid recall guarantees for source set detection.
  arXiv  Detail & Related papers  (2025-11-12T01:09:56Z)
• A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI [33.7054351451505]
  We propose a probabilistic deep learning framework based on Deep Ensembles of Mixture Density Networks (MDNs)<n>MDNs produced more calibrated and sharper predictive distributions for the diffusion coefficient D and fraction f parameters, although slight overconfidence was observed in pseudo-diffusion coefficient D*.<n>We present a comprehensive framework for IVIM fitting with uncertainty quantification, which enables the identification and interpretation of unreliable estimates.
  arXiv  Detail & Related papers  (2025-08-06T16:08:55Z)
• Uncertainty Estimation and Out-of-Distribution Detection for LiDAR Scene Semantic Segmentation [0.6144680854063939]
  Safe navigation in new environments requires autonomous vehicles and robots to accurately interpret their surroundings. We propose a method to distinguish in-distribution (ID) from out-of-distribution (OOD) samples. We quantify both epistemic and aleatoric uncertainties using the feature space of a single deterministic model.
  arXiv  Detail & Related papers  (2024-10-11T10:19:24Z)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction [63.202627467245584]
  We introduce a Bayesian variational framework to quantify the model-immanent (epistemic) uncertainty. We demonstrate that our approach yields competitive results for undersampled MRI reconstruction.
  arXiv  Detail & Related papers  (2021-02-12T18:08:14Z)
• The Hidden Uncertainty in a Neural Networks Activations [105.4223982696279]
  The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution correlates with a model's epistemic uncertainty, thus indicating its ability to generalise to novel inputs.
  arXiv  Detail & Related papers  (2020-12-05T17:30:35Z)
• Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisited [68.8204255655161]
  We introduce the CORP approach, which generates provably statistically Consistent, Optimally binned, and Reproducible reliability diagrams in an automated way. Corpor is based on non-parametric isotonic regression and implemented via the Pool-adjacent-violators (PAV) algorithm.
  arXiv  Detail & Related papers  (2020-08-07T08:22:26Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.