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

40 papers, page 1 of 1

1. NeurIPS 2026A Revisit of Hamiltonian Monte Carlo Efficiency on Bayesian Neural Networks
2. NeurIPS 2026Approximate Bayesian inference with exchangeable distributions for neurosymbolic AI
3. NeurIPS 2026Bayes-pFCL:Bayesian Personalized Federated Continual Learning
4. NeurIPS 2026Bayesian Causal Experimental Design for CATE Estimation under Noncompliance
5. NeurIPS 2026Bayesian Causal Stress Testing: Posterior Fragility of Treatment-Effect Conclusions
6. NeurIPS 2026Bayesian Low-Rank Posteriors for Scalable Membership Inference
7. NeurIPS 2026Bayesian Preference Learning for Test-Time Steerable Reward Models
8. NeurIPS 2026Bifurcation Models: Learning Set-Valued Solution Maps with Weight-Tied Dynamics
9. NeurIPS 2026BO-Arena: An Evolving Benchmark for High-Dimensional Bayesian Optimisation
10. NeurIPS 2026CLEAR: Complementary Tripartite Play with Bayesian Calibration for Semi-Supervised Edge Classification
11. NeurIPS 2026CLUE: Correlated Latent Uncertainty for Single-Pass Deep Uncertainty Estimation
12. NeurIPS 2026DnNP: Denoising Input Uncertainty in Neural Processes
13. NeurIPS 2026Does Weight Decay Enhance Training Stability?
14. NeurIPS 2026DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
15. NeurIPS 2026Dynamically Structured Diffusion Language Model Decoding via Bayesian Inference
16. NeurIPS 2026Ensemble Distributionally Robust Bayesian Optimisation
17. NeurIPS 2026ESS-Flow: training-free guidance as Bayesian inference in source space
18. NeurIPS 2026Extrapolative Weight Averaging Reveals Correctness–Efficiency Frontiers in Code RL
19. NeurIPS 2026Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions
20. NeurIPS 2026Generative Active Learning via Bayesian Acquisition for Improving the Efficiency of Synthetic Data
21. NeurIPS 2026Incorporating Neural Network Structure in the Bayesian Learning Rule
22. NeurIPS 2026Maximin Robust Bayesian Experimental Design
23. NeurIPS 2026Neural Bayesian Filtering
24. NeurIPS 2026On the Complexity of Discounted Robust MDPs with $L_p$ Uncertainty Sets
25. NeurIPS 2026On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference
26. NeurIPS 2026On the Existence of Uniformly Optimal Policies in MDPs under Epistemic Uncertainty
27. NeurIPS 2026Outlier-robust Diffusion Posterior Sampling for Bayesian Inverse Problems
28. NeurIPS 2026Position: Semantic Uncertainty Measures Disagreement, Not Reliability
29. NeurIPS 2026Preconditioned Implicit Midpoint Langevin Sampling for Non-Smooth Bayesian Imaging
30. NeurIPS 2026PRIM: Meta-Learned Bayesian Root Cause Analysis
31. NeurIPS 2026Proper Scoring Rules for Agentic Uncertainty Quantification
32. NeurIPS 2026Rethinking Bayesian Optimization for Co-Optimizing LLM Training Configurations
33. NeurIPS 2026Robust Latent Space Bayesian Optimization with Marginalized Kernel
34. NeurIPS 2026SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
35. NeurIPS 2026TabK: Amortized Bayesian Estimation of the Number of Clusters in Tabular Data
36. NeurIPS 2026Task-Driven Bayesian Experimental Design Yields Singly Intractable Objectives for Joint Policy Training
37. NeurIPS 2026The Bayesian Learning Rule Beyond KL Geometry
38. NeurIPS 2026The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities
39. NeurIPS 2026To discretize continually: Mean shift interacting particle systems for Bayesian inference
40. NeurIPS 2026Very Fast Bayesian Additive Regression Trees on GPU

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.