Bayesian Deep Learning papers, 2023–2026

Key findings

• Bayesian Deep Learning went from 112 papers in 2023 to 112 in 2026, roughly 1.0x in three years.
• NeurIPS publishes the most Bayesian Deep Learning work: 165 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with Simulation for Driving: papers tagged with both rose from 5 in 2023 to 11 in 2026.
• Among the 5 topics under Probabilistic and Bayesian, it ranks #4 by 2026 paper count; the fastest grower in the group, Gaussian Processes, is up 2.2x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Approximate Bayesian inference with exchangeable distributions for neurosymbolic AI
• ICML 2026A Bayesian Approach to Quantify the Uncertainty of Human Ratings in a Single-Instance Multimodal Framework
• ICLR 2026Bayesian Neural Networks for Functional ANOVA Model
• CVPR 2026CGU-Bayes Causal Graph Uncertainty-Guided Bayesian Inference for Domain Generalization
• NeurIPS 2025A Bayesian Approach to Contextual Dynamic Pricing using the Proportional Hazards Model with Discrete Price Data
• ICML 2025BARNN: A Bayesian Autoregressive and Recurrent Neural Network
• ICLR 2025Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior
• CVPR 2025HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories
• NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
• ICML 2024Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
• ICLR 2024A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
• CVPR 2024Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models
• NeurIPS 2023A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
• ICML 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
• ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
• CVPR 2023Bayesian Posterior Approximation With Stochastic Ensembles
• NeurIPS 2026BO-Arena: An Evolving Benchmark for High-Dimensional Bayesian Optimisation
• ICML 2026Bayesian Gated Non-Negative Contrastive Learning
• ICLR 2026Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
• CVPR 2026Evidential Deep Partial Label Learning to Quantify Disambiguation Uncertainty

All Bayesian Deep Learning papers

165 papers, page 2 of 4

51. NeurIPS 2025MOF-BFN: Metal-Organic Frameworks Structure Prediction via Bayesian Flow Networks
52. NeurIPS 2025Multidimensional Bayesian Utility Maximization: Tight Approximations to Welfare
53. NeurIPS 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
54. NeurIPS 2025One Filters All: A Generalist Filter For State Estimation
55. NeurIPS 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
56. NeurIPS 2025Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
57. NeurIPS 2025PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation
58. NeurIPS 2025Quantifying Uncertainty in the Presence of Distribution Shifts
59. NeurIPS 2025Squared families are useful conjugate priors
60. NeurIPS 2025Statistical Analysis of an Adversarial Bayesian Weak Supervision Method
61. NeurIPS 2025The Quotient Bayesian Learning Rule
62. NeurIPS 2025Torch-Uncertainty: Deep Learning Uncertainty Quantification
63. NeurIPS 2025Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
64. NeurIPS 2025TreeGen: A Bayesian Generative Model for Hierarchies
65. NeurIPS 2025Uncertainty Quantification with the Empirical Neural Tangent Kernel
66. NeurIPS 2025Uncertainty-Sensitive Privileged Learning
67. NeurIPS 2025VIKING: Deep variational inference with stochastic projections
68. NeurIPS 2024A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
69. NeurIPS 2024A Metalearned Neural Circuit for Nonparametric Bayesian Inference
70. NeurIPS 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
71. NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
72. NeurIPS 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
73. NeurIPS 2024Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
74. NeurIPS 2024Block Sparse Bayesian Learning: A Diversified Scheme
75. NeurIPS 2024BMRS: Bayesian Model Reduction for Structured Pruning
76. NeurIPS 2024Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
77. NeurIPS 2024Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
78. NeurIPS 2024Fixed Confidence Best Arm Identification in the Bayesian Setting
79. NeurIPS 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
80. NeurIPS 2024Gradients of Functions of Large Matrices
81. NeurIPS 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
82. NeurIPS 2024Hierarchical Selective Classification
83. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
84. NeurIPS 2024Nonstationary Sparse Spectral Permanental Process
85. NeurIPS 2024Poisson Variational Autoencoder
86. NeurIPS 2024Practical Bayesian Algorithm Execution via Posterior Sampling
87. NeurIPS 2024Quasi-Bayes meets Vines
88. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
89. NeurIPS 2024Sample-efficient Bayesian Optimisation Using Known Invariances
90. NeurIPS 2024SEL-BALD: Deep Bayesian Active Learning with Selective Labels
91. NeurIPS 2024Set-based Neural Network Encoding Without Weight Tying
92. NeurIPS 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
93. NeurIPS 2024The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons
94. NeurIPS 2024The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
95. NeurIPS 2024Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
96. NeurIPS 2024Unrolled denoising networks provably learn to perform optimal Bayesian inference
97. NeurIPS 2024Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance
98. NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
99. NeurIPS 2023A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
100. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods

FAQ

How many Bayesian Deep Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

408 in total: 112 in 2023, 100 in 2024, 84 in 2025, 112 in 2026.

How many Bayesian Deep Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 40 Bayesian Deep Learning papers, versus 27 the year before.

How many Bayesian Deep Learning papers are at ICLR 2026?

ICLR 2026 has 22 Bayesian Deep Learning papers, versus 30 the year before.

How many Bayesian Deep Learning papers are at ICML 2026?

ICML 2026 has 41 Bayesian Deep Learning papers, versus 24 the year before.

How many Bayesian Deep Learning papers are at CVPR 2026?

CVPR 2026 has 9 Bayesian Deep Learning papers, versus 3 the year before.

Which conference publishes the most Bayesian Deep Learning papers?

NeurIPS, with 165 papers from 2023 to 2026.