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

112 papers, page 2 of 3

51. NeurIPS 2023Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
52. NeurIPS 2023Bayesian Learning via Q-Exponential Process
53. NeurIPS 2023Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
54. NeurIPS 2023Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability
55. NeurIPS 2023Bayesian Optimisation of Functions on Graphs
56. NeurIPS 2023Bayesian Optimization with Cost-varying Variable Subsets
57. NeurIPS 2023Bayesian Risk-Averse Q-Learning with Streaming Observations
58. NeurIPS 2023Bayesian target optimisation for high-precision holographic optogenetics
59. NeurIPS 2023BayesTune: Bayesian Sparse Deep Model Fine-tuning
60. NeurIPS 2023Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
61. NeurIPS 2023Beyond probability partitions: Calibrating neural networks with semantic aware grouping
62. NeurIPS 2023Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
63. NeurIPS 2023Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff
64. NeurIPS 2023Bounded rationality in structured density estimation
65. NeurIPS 2023Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
66. NeurIPS 2023Collapsed Inference for Bayesian Deep Learning
67. NeurIPS 2023Compression with Bayesian Implicit Neural Representations
68. NeurIPS 2023Conditional score-based diffusion models for Bayesian inference in infinite dimensions
69. NeurIPS 2023Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
70. NeurIPS 2023Distributionally Robust Bayesian Optimization with $\varphi$-divergences
71. NeurIPS 2023Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
72. NeurIPS 2023Dynamic Pricing and Learning with Bayesian Persuasion
73. NeurIPS 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
74. NeurIPS 2023Easy Bayesian Transfer Learning with Informative Priors
75. NeurIPS 2023Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
76. NeurIPS 2023Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
77. NeurIPS 2023Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs
78. NeurIPS 2023Epistemic Neural Networks
79. NeurIPS 2023Federated Learning via Meta-Variational Dropout
80. NeurIPS 2023Flat Seeking Bayesian Neural Networks
81. NeurIPS 2023Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
82. NeurIPS 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
83. NeurIPS 2023Generalized Belief Transport
84. NeurIPS 2023Gradient-Free Kernel Stein Discrepancy
85. NeurIPS 2023Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
86. NeurIPS 2023Implicit Manifold Gaussian Process Regression
87. NeurIPS 2023Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
88. NeurIPS 2023Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization
89. NeurIPS 2023Learning Layer-wise Equivariances Automatically using Gradients
90. NeurIPS 2023Learning Rate Free Sampling in Constrained Domains
91. NeurIPS 2023Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
92. NeurIPS 2023On permutation symmetries in Bayesian neural network posteriors: a variational perspective
93. NeurIPS 2023Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
94. NeurIPS 2023PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
95. NeurIPS 2023Percentile Criterion Optimization in Offline Reinforcement Learning
96. NeurIPS 2023PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
97. NeurIPS 2023Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
98. NeurIPS 2023Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
99. NeurIPS 2023Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
100. NeurIPS 2023Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning

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.