arXiv AI

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

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.

Hugging Face Trending Papers

Jul 7

Efficient Bayesian Deep Ensembles via Analytic Predictive Inference

We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency. Our method combines the statistical rigor of Bayesian inference with the scalability of deep ensembles, providing calibrated uncertainty estimates that enable its use not only for standalone prediction but also as a component within broader learning systems.

benchmarks safety

arXiv Machine Learning

Jun 8

Conformal Disentanglement and Latent-Space Curation: A Neural Framework for Perspective Synthesis, Differentiation and Targeted Generation

arXiv:2408. 15344v2 Announce Type: replace Abstract: Many scientific and engineering problems involve observing a common phenomenon through multiple heterogeneous sensors or measurement modalities.

By George A. Kevrekidis, Eleni D. Koronaki, Dimitris G. Giovanis, Yannis G. Kevrekidis

arXiv Machine Learning

Jul 31

Uncertainty quantification for trustworthy deep learning: Methods and measures

arXiv:2607. 28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification.

By H. Martin Gillis, Thomas Trappenberg

llms safety

arXiv Machine Learning

Jul 9

Efficient Bayesian Deep Ensembles via Analytic Predictive Inference

arXiv:2607. 06776v1 Announce Type: new Abstract: We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency.

By Sina Aghaee Dabaghan Fard, Marie Maros, Jaesung Lee

benchmarks safety

arXiv Machine Learning

Aug 3

Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties

arXiv:2505. 02743v3 Announce Type: replace Abstract: Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data generation process.

By Jiaxiang Yi, Miguel A. Bessa

arXiv Machine Learning

Aug 10

Conformal Fusion Under Missing Modalities

arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.

By Alireza Moayedikia

multimodal benchmarks

arXiv Machine Learning

Jun 26

Learning Probabilistic Filters with Strictly Proper Scoring Rules

arXiv:2606. 26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system, given observations, in an online fashion.

By Eviatar Bach, Ricardo Baptista, Jochen Br\"ocker, Bohan Chen, Andrew Stuart

llms multimodal safety

arXiv Machine Learning

Jul 24

Generative Bayesian Filtering for State Estimation

arXiv:2607. 20521v1 Announce Type: new Abstract: The state of a dynamic system evolves over time, switching among several latent modes that govern its observable behavior.

By Lei Cao, Sihang Feng, Jixin Yan, Tao Sun, Naichen Shi

arXiv Machine Learning

Aug 4

An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusion

arXiv:2605. 22259v2 Announce Type: replace Abstract: Heterogeneous sensor fusion is vital for detecting, localizing, and classifying CBRNE threats.

By Jan Nausner, Michael Hubner

arXiv AI

Jun 2

Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics

arXiv:2606. 01468v1 Announce Type: cross Abstract: Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dynamical latent variable modeling of single-cell neural recordings.

By JR Huml, Jonathan Wenger, John P. Cunningham

arXiv Machine Learning

Jun 4

Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation

arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.

By H. Martin Gillis, Isaac Xu, Thomas Trappenberg

benchmarks

arXiv Machine Learning

Oct 5

A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants

A new framework for Bayesian data assimilation is presented that leverages pretrained stochastic interpolants, flow matching, and diffusion models as posterior samplers without retraining. By conditioning the interpolant path on observations, a shared likelihood‑score correction is applied to the drift or velocity, unifying stochastic and deterministic sampling methods. The approach yields exact posterior samples when the intermediate likelihood score is known, and practical approximations use a Gaussian surrogate with bias‑corrected mean and inflated covariance, enabling tractable high‑dimensional applications such as linear‑Gaussian dynamics, stochastic Navier‑Stokes, and urban airflow simulations with up to $O(10^4)$ degrees of freedom.

By Nikolaj T. M\"ucke, Benjamin Sanderse

diffusion safety data-engineering

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