Bayesian Neural Networks

Also known as BNN

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Minimaxity and Admissibility of Bayesian Neural Networks
Apr 6, 2026Daniel Andrew Coulson, Martin T. WellsBayesian Neural Networks Minimax Estimation
Department of Statistics and Data Science, Cornell University
Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method
Mar 26, 2026Chenxu Yu, Wenqi FangBayesian Neural Networks Neural Network Optimization
Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen, P.R.China
On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
Mar 23, 2026Julius Kobialka, Emanuel Sommer, Chris Kolb +3Bayesian Neural Networks
LMU Munich, MCML · HTWG Konstanz
CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis
Mar 23, 2026Shailesh Garg, Souvik ChakrabortyUncertainty Quantification Bayesian Neural Networks
Department of Applied Mechanics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. · Department of Applied Mechanics, Yardi School of Artificial Intelligence (YScAI), Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
Spatio-temporal probabilistic forecast using MMAF-guided learning
Mar 16, 2026Leonardo Bardi, Imma Valentina Curato, Lorenzo ProiettiBayesian Neural Networks Spatiotemporal Forecasting
Department of Mathematics TU Chemnitz Reichenhainer Straße 41
Beyond Accuracy: Reliability and Uncertainty Estimation in Convolutional Neural Networks
Mar 11, 2026Sanne Ruijs, Alina Kosiakova, Farrukh JavedUncertainty Quantification Uncertainty Calibration
Department of Economics, Lund University, Sweden · Department of Statistics, Lund University, Sweden
Towards Reliable Simulation-based Inference
Mar 9, 2026Arnaud DelaunoySimulation-Based Inference Probability Calibration
Department of Electrical Engineering and Computer Science, Faculty of Applied Sciences
Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data
Mar 9, 2026L. Julián Lechuga López, Tim G. J. Rudner, Farah E. ShamoutBayesian Neural Networks Uncertainty Quantification
Tandon School of Engineering, New York University · New York University Abu Dhabi · University of Toronto
Bayesian Transformer for Probabilistic Load Forecasting in Smart Grids
Mar 9, 2026Sajib Debnath, Md. Uzzal MiaElectricity Load Forecasting Probabilistic Time Series Forecasting
O&M Analytics, AES Clean Energ; The AES Corporation; Louisville, CO 80027, USA · Department of Information and Communication Engineering; Pabna University of Science and Technology; Rajapur, Pabna, Bangladesh
Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
Mar 3, 2026Mabel Heffring, Lincoln Linlin XuRemote Sensing Uncertainty Quantification
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, T2N 1N4, AB, Canada
Probabilistic Learning and Generation in Deep Sequence Models
Mar 1, 2026Wenlong ChenBayesian Inference Sequence Modeling
Imperial College London Department of Computing
Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks
Feb 26, 2026Katerina Papagiannouli, Dario Trevisan, Giuseppe Pio ZittoBayesian Neural Networks Bayesian Inference
Dipartimento di Matematica, Università di Pisa
From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
Feb 26, 2026Gracielle Antunes de Araújo, Flávio B. GonçalvesBayesian Neural Networks Shallow Neural Networks
Universidade do Estado de Minas Gerais (UEMG), FaEnge – Faculdade de Engenharia. · Universidade Federal de Minas Gerais (UFMG), ICEx – Instituto de Ciências Exatas.
Function-Space Empirical Bayes Regularisation with Student's t Priors
Feb 25, 2026Pengcheng Hao, Ercan Engin KuruogluBayesian Neural Networks Bayesian Inference
Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Shenzhen, China
Compact Circulant Layers with Spectral Priors
Feb 25, 2026Joseph Margaryan, Thomas HamelryckNeural Network Compression Bayesian Neural Networks
Department of Computer Science (DIKU), University of Copenhagen, Copenhagen, Denmark · Department of Biology, University of Copenhagen, Copenhagen, Denmark
Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions
Feb 24, 2026Mame Diarra Toure, David A. StephensBayesian Neural Networks Classification
Department of Mathematics and Statistics , McGill University
Sparse Bayesian Deep Functional Learning with Structured Region Selection
Feb 24, 2026Xiaoxian Zhu, Yingmeng Li, Shuangge Ma +1Bayesian Neural Networks Feature Selection
School of Statistics and Data Science, Shanghai University of Finance and Economics · Department of Biostatistics, Yale School of Public Health
Dirichlet Scale Mixture Priors for Bayesian Neural Networks
Feb 23, 2026August Arnstad, Leiv Rønneberg, Geir StorvikBayesian Neural Networks Sparse Neural Networks
Department of Statistics & Data Science, University of Oslo.
Bayesian Lottery Ticket Hypothesis
Feb 21, 2026Nicholas Kuhn, Arvid Weyrauch, Lars Heyen +3Sparse Neural Networks Bayesian Neural Networks
Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany · Helmholtz AI, Eggenstein-Leopoldshafen, Germany
Confidence-Uncertainty Boundary Calibration for Bayesian Deep Learning in Medical Image Analysis
Feb 12, 2026Hua Xu, Julián D. Arias-Londoño, Juan I. Godino-LlorenteBayesian Neural Networks Post-Hoc Calibration
Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid 28040, Madrid, Spain
Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
Jan 28, 2026Moule Lin, Shuhao Guan, Andrea Patane +2Bayesian Neural Networks Confidence Estimation in Language Models
School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland and Lero the Research Ireland Centre for Software, Ireland · School of Computer Science, University College Dublin, Dublin, Ireland
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
Jan 7, 2026Ibai Ramirez, Jokin Alcibar, Joel Pino +2Bayesian Neural Networks Uncertainty Quantification
Department of Electronics and Computer Science Mondragon University Arrasate-Mondragon, 20500, Spain · CARTIF Technology Centre Valladolid, 47151, Spain · Department of Physical Chemistry University of the Basque Country (EHU)[cs.LG] Basque Centre for Applied Mathematics (BCAM) Leioa, 20018, Spain +1
Bayesian Speech Synthesizers Can Learn from Multiple Teachers
Oct 28, 2025Ziyang Zhang, Yifan Gao, Xuenan Xu +3TTS Synthesis Bayesian Neural Networks
Tsinghua University · Shanghai Artificial Intelligence Laboratory.
Last Layer Hamiltonian Monte Carlo
Jul 11, 2025Koen Vellenga, H. Joe Steinhauer, Göran Falkman +2Bayesian Neural Networks Markov Chain Monte Carlo
University of Skövde & Volvo Car Corporation, Sweden · University of Skövde, Sweden · Volvo Car Corporation, Sweden
Bayesian Deep Learning for Discrete Choice
May 23, 2025Daniel F. Villarraga, Ricardo A. DazianoBayesian Neural Networks Bayesian Inference
School of Civil and Environmental Engineering, Cornell University, 220 Hollister Hall, Ithaca, NY 14853, USA
Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
Mar 13, 2025Eirik Høyheim, Lars Skaaret-Lund, Solve Sæbø +1Bayesian Neural Networks Uncertainty Quantification
Bioinformatics and applied statistics (BIAS), Norwegian University of Life Sciences, Ås, Norway

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Minimaxity and Admissibility of Bayesian Neural Networks

Daniel Andrew Coulson, Martin T. Wells

Department of Statistics and Data Science, Cornell University

Bayesian Neural Networks Minimax Estimation Bayesian Decision Theory

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Bayesian neural networks (BNNs) offer a natural probabilistic formulation for inference in deep learning models. Despite their popularity, their optimality has received limited attention through the lens of statistical decision theory. In this paper, we study decision rules induced by deep, fully connected feedforward ReLU BNNs in the normal location model under quadratic loss. We show that, for fixed prior scales, the induced Bayes decision rule is not minimax. We then propose a hyperprior on the effective output variance of the BNN prior that yields a superharmonic square-root marginal density, establishing that the resulting decision rule is simultaneously admissible and minimax. We further extend these results from the quadratic loss setting to the predictive density estimation problem with Kullback--Leibler loss. Finally, we validate our theoretical findings numerically through simulation.