Latest Bayesian Deep Learning Research Papers

The newest Bayesian Deep Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Bayesian Deep Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Hidden Disparities in Ranking: Category-Aware Fairness via Distributional Stability
  Gayane Taturyan, Charlotte Laclau, Stephan Clémençon · arXiv · Oct 29, 2026

  We study the problem of fair ranking in recommender systems. We provide a formal probabilistic framework in which ranking policies are distributions over permutations, and the utility of a user is the exposure given to the items they intera…

• Probabilistic Meshless Methods on Manifolds
  Federico Pavesi, Noémie Jaquier, Antonio Candelieri · arXiv · Oct 29, 2026

  Probabilistic meshless methods solve partial differential equations (PDEs) by placing a Gaussian process prior on the solution and conditioning it on the PDE at a finite set of collocation nodes. The posterior provides both an approximation…

• Emergence of the transformer architecture as the score of entropic laws of random measures
  Giacomo Greco · arXiv · Oct 29, 2026

  We show that the transformer architecture emerges as the score of last-token laws in a natural statistical model of language, where sentences are empirical measures of tokens, a language is a law on $P(S^d)$, and our prior belief is that se…

• TabCUQ: Context-Uncertainty Quantification for Tabular Foundation Models
  Yuebei Shi, Molei Liu, Zijian Guo · arXiv · Oct 29, 2026

  Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatment effects for specific groups of individuals. Using these estimates to guide scientific decisions requires reliable uncertainty …

• Non-Asymptotic Confidence Regions for Separable Nonlinear Models
  Szabolcs Szentpéteri, Péter Kovács, Balázs Csanád Csáji · arXiv · Oct 29, 2026

  This paper studies robust uncertainty quantification for separable nonlinear least squares (SNLLS) problems. We propose two identification methods, (i) Residual-Perturbed Gradient (RPG) and (ii) Global-Fit-Gap (GFG), to construct joint dist…

• Modular Bayesian Foundation Models: Bayesian time series encoders for OOD detection, routing and forecasting
  Mame Diarra Toure, Quang Hung Pham, Mohamed Ryad Zemouri, David Stephens et al. · arXiv · Oct 28, 2026

  Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and operating regime. Consequently a single time-series foundation model may not be able to represent all regimes effectively and retrain…

• QD-PINN:A Physics-Informed Neural Network Approach to Bayesian Quickest Detection in High-Dimensional Diffusions
  Hang Ye, Yi Liu, Haipeng Xing · arXiv · Oct 27, 2026

  Quickest detection problems (QDPs) arise in a wide range of applications. However, the complementarity structure of the associated variational inequalities poses substantial challenges for traditional numerical methods, particularly in high…

• An end-to-end framework for uncertainty-aware validation of generative models
  Giada Badaracco, Sean Benevedes, Christina Reissel, Philip Harris et al. · arXiv · Oct 27, 2026

  Density estimation with generative models is a common task in the physical sciences, for example in background estimation in particle physics. Existing machine learning techniques, however, lack methods to quantify uncertainties that can be…

• Rènyi-Regularized Flow-Based Variational Inference for Bayesian Calibration
  Exaucè Luweh Adjim Ngarti, Élise Arnaud, Léo Nicoletti, Victor Trappler et al. · arXiv · Oct 27, 2026

  Variational inference with normalizing flows provides a flexible posterior approximation for Bayesian model calibration, but struggles when the posterior has several modes separated by regions of low probability mass. We study this behavior…

• LLM knowledge for improved Out-of-Distribution Tabular Prediction - A Cross-Validated Regularisation Approach
  Sølve Langseth, Kristoffer Herland Hellton, Hugo L. Hammer · arXiv · Oct 27, 2026

  Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer. Expert elicitation is slow and unscalable. We ask whether coe…

• Deep Bayesian Approaches for Interpretable Nonlinear Structural System Identification
  Wei Liu, Konstantinos Vlachas, Eleni Chatzi · arXiv · Oct 27, 2026

  System identification, namely the inference of mathematical representations of dynamical systems from measured data, is fundamental to engineering diagnostics, prognostics, and decision-making. Bayesian filtering offers a principled framewo…

• Dirichlet Process Mixtures within Semiparametric Bayesian Networks: A Novel Conditional Density Estimation Approach
  Victor Alejandre, Pedro Larrañaga, Concha Bielza · arXiv · Oct 26, 2026

  Semiparametric Bayesian networks (SPBNs) are flexible probabilistic graphical models for representing joint distributions of continuous variables through lower-dimensional conditional probability distributions (CPDs). Existing SPBN models u…

• Prior Predictive Matching for Bayesian LoRA
  Tomasz Kuśmierczyk, Eliezer de Souza da Silva, Marcin Sendera · arXiv · Oct 26, 2026

  Bayesian variants of Low-Rank Adaptation (LoRA) provide principled uncertainty estimation when fine-tuning large foundation models, but require specifying the prior over the low-rank weight updates. We address this hyperparameter selection …

• A knowledge-driven nonconvex square-root regularization framework for joint group sparsity and structured smoothness
  Wanling Xie, Hu Yang · arXiv · Oct 26, 2026

  Structured sparse learning is a leading paradigm for high-dimensional data modeling, as it enables rigorous integration of domain-validated structural priors into the learning process. Among these priors, pre-defined grouped structure and p…

• On the asymptotic behaviour of $α$-divergence generalized posteriors
  Ioar Casado-Telletxea, Omar Rivasplata · arXiv · Oct 26, 2026

  We study the asymptotic behaviour of the solutions to the unconstrained generalized variational inference problem regularized by an $α$-divergence. Under mild conditions, the minimizer is explicit up to a scalar normalizing constant, and we…

• Intrinsic Information Rates of Summaries in Simulation-Based Inference
  Vansh Bansal, Pradip Sharma Poudel, James G. Scott · arXiv · Oct 26, 2026

  Simulation-based inference often encodes observations $Y$ into a fixed-dimensional summary $S$, with the posterior conditioning only on $S$. While exact preservation often requires infinite information, any such summary imposes a finite inf…

• Quantitative mobile gamma-ray spectrometry through Bayesian inference
  David Breitenmoser, Alberto Stabilini, M. Kasprzak, Sabine Mayer · Nature Communications · Oct 26, 2026

  Abstract Accurate quantitative mapping of gamma-ray emitters is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that such mapp…

• Calibrated Bayes Analysis of Cluster‐Randomized Trials
  Ruyi Liu, Joshua L. Warren, Yuki Ohnishi, Donna L. Spiegelman et al. · Statistics in Medicine · Oct 26, 2026

  ABSTRACT In cluster‐randomized trials (CRTs), entire clusters of individuals are randomized to treatment, and outcomes within a cluster are typically correlated. While frequentist approaches are standard practice for CRT analysis, Bayesian …

• A Bayesian functional model with multilevel partition priors for group studies in neuroscience
  Nicolò Margaritella, Vanda Inácio, Ruth King · The International Journal o... · Oct 25, 2026

  The statistical analysis of group studies in neuroscience is particularly challenging due to the complex spatio-temporal nature of the data, its multiple levels and the inter-individual variability in brain responses. Traditional ANOVA-base…

• Eyegent: Visual Attention Orchestration on Concepts
  Alessandro D’Amelio, Sabrina Patania, Luca Annese, Giuseppe Boccignone · International Journal of Co... · Oct 25, 2026

  Abstract Where a human looks is not random, nor is it merely a reflex to visual salience. It is a decision shaped by goals, constrained by language, and driven by the need to resolve uncertainty. We propose Eyegent , an active vision agent …

• Multi-objective artificial pedosphere intelligence for microbial homeostasis-oriented governance of functional nanoparticle systems in complex ecosystems
  Raju Vidap, Ranjith Kumar Nadialli, VINOD KUMAR TERIVEEDHI, K. Suresh Babu et al. · Scientific Reports · Oct 25, 2026

  Abstract Soil microbial homeostasis is fundamental to sustainable agriculture and ecological resilience, yet existing machine learning approaches primarily emphasize prediction accuracy while overlooking microbial interactions, uncertainty …

• Bayesian optimization of super-resolution for crop disease detection
  Deekshitha Arasa, Sneha Sharma, S. Sivaramakrishnan · International Journal of Re... · Oct 25, 2026

  This work proposes a Bayesian hyperparameter optimization framework for enhanced deep super-resolution networks (EDSR) to improve lesion-aware disease classification. A 4× degradation model was applied to PlantVillage images, and Optuna was…

• Emerging trends in AI-driven mining hazard management
  Chaoran Huang, Ruiyu Liang, Chengguo Zhang, Ismet Canbulat et al. · Earth energy science. · Oct 24, 2026

  The management of geotechnical hazards in mining is critical for ensuring operational safety and sustainability, yet its practical implementation often struggles with the inherent geological uncertainty and the dynamic, non-linear nature of…

• Drivers of supply chain resilience in the new energy vehicle industry: An explainable machine learning approach
  Yang Jiang, Chenchen Li, Jijing Hang · Energy Reports · Oct 24, 2026

  Under the global dual-carbon goals, supply chain resilience has become a strategic priority for the new energy vehicle (NEV) industry. Using a 2016–2023 panel of 403 Chinese listed NEV firms, we construct an entropy-weighted supply chain re…

• When active learning fails, uncalibrated out of distribution uncertainty quantification might be the problem
  Ashley S. Dale, Kangming Li, Brian DeCost, Hao Wan et al. · npj Computational Materials · Oct 24, 2026

  Abstract Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing information missing …

• Sparse Bayesian Neural Networks via Sticky Piecewise Deterministic Markov Processes
  August Arnstad, Geir Olve Storvik, Filippo Pagani, Leiv Rønneberg · arXiv · Oct 23, 2026

  There is growing evidence that the performance of a neural network depends largely on a small subset of its weights, which are typically identified by training a dense network and pruning it afterwards. Within a Bayesian framework, pruning …

• Acquisition for Spherical-Linear Bayesian Optimization on the Cube
  Nathan Wycoff · arXiv · Oct 23, 2026

  Recent work demonstrated that executing Bayesian Optimization (BO) using simply a linear surrogate with a clever transformation is enough to approximate the performance of the standard Gaussian-Process (GP) based version on high-dimensional…

• Contextual Decision Making: Predictive or Generative
  L. Jeff Hong, Weihuan Huang, Xinyao Li · arXiv · Oct 23, 2026

  Contextual decision making uses observed covariates to guide decisions under uncertainty. Two natural approaches are predict-then-optimize (PtO), which learns the conditional expected objective, and generate-then-optimize (GtO), which learn…

• Data Quality Audit and Bivariate Temperature-Humidity Analysis of a Long-Term LoRaWAN Network: Anomaly Detection, PCA, Clustering and Machine Learning
  Mamadou Sow · arXiv · Oct 23, 2026

  Long-term environmental IoT/LPWAN deployments often aggregate heterogeneous data streams from diverse devices without systematic auditing of each variable's actual coverage prior to analysis-an increased risk when multiple sensor types coex…

• Federated Nonparametric Regression with Heterogeneous Clients
  Xiaoran Jiang, Anand N. Vidyashankar · arXiv · Oct 23, 2026

  Whether a federated predictor benefits an individual client depends on both population heterogeneity and estimation uncertainty. We study this question for Nadaraya--Watson regression in a triangular array with potentially different fitting…