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

97 papers, page 1 of 2

1. ICLR 2026A Fair Bayesian Inference through Matched Gibbs Posterior
2. ICLR 2026Bayesian Neural Networks for Functional ANOVA Model
3. ICLR 2026Bayesian Post Training Enhancement of Regression Models with Calibrated Rankings
4. ICLR 2026Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
5. ICLR 2026Compositional amortized inference for large-scale hierarchical Bayesian models
6. ICLR 2026Don’t Pass@$k$: A Bayesian Framework for Large Language Model Evaluation
7. ICLR 2026Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
8. ICLR 2026Enhancing Hallucination Detection through Noise Injection
9. ICLR 2026Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers
10. ICLR 2026EUBRL: Epistemic Uncertainty Directed Bayesian Reinforcement Learning
11. ICLR 2026Federated ADMM from Bayesian Duality
12. ICLR 2026Optimizing Data Augmentation through Bayesian Model Selection
13. ICLR 2026Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure
14. ICLR 2026Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
15. ICLR 2026Robust Decision-Making with Partially Calibrated Forecasters
16. ICLR 2026Semantic Uncertainty Quantification of Hallucinations in LLMs: A Quantum Tensor Network Based Method
17. ICLR 2026Singleton-Optimized Conformal Prediction
18. ICLR 2026Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
19. ICLR 2026The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
20. ICLR 2026Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
21. ICLR 2026Uncertainty Estimation via Hyperspherical Confidence Mapping
22. ICLR 2026Variational Deep Learning via Implicit Regularization
23. ICLR 2025A Meta-Learning Approach to Bayesian Causal Discovery
24. ICLR 2025Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching: With Insights into Other Permutation Search Methods
25. ICLR 2025Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior
26. ICLR 2025Bayesian Optimization via Continual Variational Last Layer Training
27. ICLR 2025Calibrating Expressions of Certainty
28. ICLR 2025Causal Discovery via Bayesian Optimization
29. ICLR 2025CBMA: Improving Conformal Prediction through Bayesian Model Averaging
30. ICLR 2025Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
31. ICLR 2025Deep Kernel Posterior Learning under Infinite Variance Prior Weights
32. ICLR 2025Deep Random Features for Scalable Interpolation of Spatiotemporal Data
33. ICLR 2025Dimension Agnostic Neural Processes
34. ICLR 2025Distribution-Free Data Uncertainty for Neural Network Regression
35. ICLR 2025ELBOing Stein: Variational Bayes with Stein Mixture Inference
36. ICLR 2025Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer
37. ICLR 2025From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
38. ICLR 2025Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
39. ICLR 2025KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks
40. ICLR 2025Laplace Sample Information: Data Informativeness Through a Bayesian Lens
41. ICLR 2025Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
42. ICLR 2025Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
43. ICLR 2025Provable Uncertainty Decomposition via Higher-Order Calibration
44. ICLR 2025Random-Set Neural Networks
45. ICLR 2025Residual Deep Gaussian Processes on Manifolds
46. ICLR 2025Robust Conformal Prediction with a Single Binary Certificate
47. ICLR 2025Scalable Bayesian Learning with posteriors
48. ICLR 2025Searching for Optimal Solutions with LLMs via Bayesian Optimization
49. ICLR 2025Streamlining Prediction in Bayesian Deep Learning
50. ICLR 2025Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations

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.