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

100 papers, page 2 of 2

51. ICML 2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
52. ICML 2024Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
53. ICML 2024Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
54. ICML 2024Learning to Continually Learn with the Bayesian Principle
55. ICML 2024MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
56. ICML 2024More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms
57. ICML 2024On the Weight Dynamics of Deep Normalized Networks
58. ICML 2024PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models
59. ICML 2024PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
60. ICML 2024Partially Stochastic Infinitely Deep Bayesian Neural Networks
61. ICML 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
62. ICML 2024Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
63. ICML 2024Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
64. ICML 2024Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks
65. ICML 2024Second-Order Uncertainty Quantification: A Distance-Based Approach
66. ICML 2024The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks
67. ICML 2024Transitional Uncertainty with Layered Intermediate Predictions
68. ICML 2024Uncertainty Estimation by Density Aware Evidential Deep Learning
69. ICML 2024Variational Linearized Laplace Approximation for Bayesian Deep Learning
70. ICML 2024WARM: On the Benefits of Weight Averaged Reward Models
71. NeurIPS 2024A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
72. NeurIPS 2024A Metalearned Neural Circuit for Nonparametric Bayesian Inference
73. NeurIPS 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
74. NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
75. NeurIPS 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
76. NeurIPS 2024Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
77. NeurIPS 2024Block Sparse Bayesian Learning: A Diversified Scheme
78. NeurIPS 2024BMRS: Bayesian Model Reduction for Structured Pruning
79. NeurIPS 2024Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
80. NeurIPS 2024Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
81. NeurIPS 2024Fixed Confidence Best Arm Identification in the Bayesian Setting
82. NeurIPS 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
83. NeurIPS 2024Gradients of Functions of Large Matrices
84. NeurIPS 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
85. NeurIPS 2024Hierarchical Selective Classification
86. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
87. NeurIPS 2024Nonstationary Sparse Spectral Permanental Process
88. NeurIPS 2024Poisson Variational Autoencoder
89. NeurIPS 2024Practical Bayesian Algorithm Execution via Posterior Sampling
90. NeurIPS 2024Quasi-Bayes meets Vines
91. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
92. NeurIPS 2024Sample-efficient Bayesian Optimisation Using Known Invariances
93. NeurIPS 2024SEL-BALD: Deep Bayesian Active Learning with Selective Labels
94. NeurIPS 2024Set-based Neural Network Encoding Without Weight Tying
95. NeurIPS 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
96. NeurIPS 2024The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons
97. NeurIPS 2024The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
98. NeurIPS 2024Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
99. NeurIPS 2024Unrolled denoising networks provably learn to perform optimal Bayesian inference
100. NeurIPS 2024Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance

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.