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

30 papers, page 1 of 1

1. NeurIPS 2024A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
2. NeurIPS 2024A Metalearned Neural Circuit for Nonparametric Bayesian Inference
3. NeurIPS 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
4. NeurIPS 2024Bayesian Identification of the Hamiltonian Inductive Bias in Dynamical Systems
5. NeurIPS 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
6. NeurIPS 2024Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
7. NeurIPS 2024Block Sparse Bayesian Learning: A Diversified Scheme
8. NeurIPS 2024BMRS: Bayesian Model Reduction for Structured Pruning
9. NeurIPS 2024Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
10. NeurIPS 2024Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
11. NeurIPS 2024Fixed Confidence Best Arm Identification in the Bayesian Setting
12. NeurIPS 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
13. NeurIPS 2024Gradients of Functions of Large Matrices
14. NeurIPS 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
15. NeurIPS 2024Hierarchical Selective Classification
16. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
17. NeurIPS 2024Nonstationary Sparse Spectral Permanental Process
18. NeurIPS 2024Poisson Variational Autoencoder
19. NeurIPS 2024Practical Bayesian Algorithm Execution via Posterior Sampling
20. NeurIPS 2024Quasi-Bayes meets Vines
21. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
22. NeurIPS 2024Sample-efficient Bayesian Optimisation Using Known Invariances
23. NeurIPS 2024SEL-BALD: Deep Bayesian Active Learning with Selective Labels
24. NeurIPS 2024Set-based Neural Network Encoding Without Weight Tying
25. NeurIPS 2024Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
26. NeurIPS 2024The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons
27. NeurIPS 2024The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
28. NeurIPS 2024Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
29. NeurIPS 2024Unrolled denoising networks provably learn to perform optimal Bayesian inference
30. 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.