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 1 of 3

1. CVPR 2023Bayesian Posterior Approximation With Stochastic Ensembles
2. CVPR 2023Deep Deterministic Uncertainty: A New Simple Baseline
3. CVPR 2023Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning
4. CVPR 2023Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization
5. CVPR 2023Spectral Bayesian Uncertainty for Image Super-Resolution
6. CVPR 2023Training Debiased Subnetworks With Contrastive Weight Pruning
7. ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
8. ICLR 2023Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation
9. ICLR 2023Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle
10. ICLR 2023Approximate Bayesian Inference with Stein Functional Variational Gradient Descent
11. ICLR 2023Batch Multivalid Conformal Prediction
12. ICLR 2023BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging
13. ICLR 2023Calibrating Transformers via Sparse Gaussian Processes
14. ICLR 2023Deep Variational Implicit Processes
15. ICLR 2023Latent Neural ODEs with Sparse Bayesian Multiple Shooting
16. ICLR 2023Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
17. ICLR 2023MARS: Meta-learning as Score Matching in the Function Space
18. ICLR 2023Martingale Posterior Neural Processes
19. ICLR 2023Model-based Causal Bayesian Optimization
20. ICLR 2023Robustness to corruption in pre-trained Bayesian neural networks
21. ICLR 2023SAM as an Optimal Relaxation of Bayes
22. ICLR 2023Sampling-based inference for large linear models, with application to linearised Laplace
23. ICLR 2023Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
24. ICLR 2023Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
25. ICLR 2023Transfer NAS with Meta-learned Bayesian Surrogates
26. ICML 2023Adversarial robustness of amortized Bayesian inference
27. ICML 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
28. ICML 2023BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
29. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
30. ICML 2023Differentially Private Distributed Bayesian Linear Regression with MCMC
31. ICML 2023Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
32. ICML 2023End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
33. ICML 2023Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
34. ICML 2023Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
35. ICML 2023Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
36. ICML 2023MEWL: Few-shot multimodal word learning with referential uncertainty
37. ICML 2023On the Complexity of Bayesian Generalization
38. ICML 2023PAC-Bayesian Offline Contextual Bandits With Guarantees
39. ICML 2023PFNs4BO: In-Context Learning for Bayesian Optimization
40. ICML 2023Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
41. ICML 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
42. ICML 2023Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
43. ICML 2023spred: Solving L1 Penalty with SGD
44. ICML 2023Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
45. NeurIPS 2023“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
46. NeurIPS 2023A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
47. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
48. NeurIPS 2023Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
49. NeurIPS 2023Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
50. NeurIPS 2023Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors

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.