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

408 papers, page 4 of 9

151. ICML 2025Conformal Prediction as Bayesian Quadrature
152. ICML 2025Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
153. ICML 2025Deep Bayesian Filter for Bayes-Faithful Data Assimilation
154. ICML 2025Do Bayesian Neural Networks Actually Behave Like Bayesian Models?
155. ICML 2025FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
156. ICML 2025GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras
157. ICML 2025Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference
158. ICML 2025Improving Generalization with Flat Hilbert Bayesian Inference
159. ICML 2025Latent Variable Estimation in Bayesian Black-Litterman Models
160. ICML 2025LEAPS: A discrete neural sampler via locally equivariant networks
161. ICML 2025Learning Bayesian Nash Equilibrium in Auction Games via Approximate Best Response
162. ICML 2025Learning Gaussian DAG Models without Condition Number Bounds
163. ICML 2025Linearization Turns Neural Operators into Function-Valued Gaussian Processes
164. ICML 2025Maximizing Intermediate Checkpoint Value in LLM Pretraining with Bayesian Optimization
165. ICML 2025Models of Heavy-Tailed Mechanistic Universality
166. ICML 2025Position: The Future of Bayesian Prediction Is Prior-Fitted
167. ICML 2025Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
168. ICML 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
169. ICML 2025Scalable Gaussian Processes with Latent Kronecker Structure
170. NeurIPS 2025A Bayesian Approach to Contextual Dynamic Pricing using the Proportional Hazards Model with Discrete Price Data
171. NeurIPS 2025A Bayesian Fast-Slow Framework to Mitigate Interference in Non-Stationary Reinforcement Learning
172. NeurIPS 2025Bayesian Concept Bottleneck Models with LLM Priors
173. NeurIPS 2025BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
174. NeurIPS 2025Diffusion Models Meet Contextual Bandits
175. NeurIPS 2025DSCS: Fast CPDAG-Based Verification of Collapsible Submodels in High-Dimensional Bayesian Networks
176. NeurIPS 2025Geometric Algebra-Enhanced Bayesian Flow Network for RNA Inverse Design
177. NeurIPS 2025Large Language Bayes
178. NeurIPS 2025Markov Persuasion Processes: Learning to Persuade From Scratch
179. NeurIPS 2025Model-Informed Flows for Bayesian Inference
180. NeurIPS 2025MOF-BFN: Metal-Organic Frameworks Structure Prediction via Bayesian Flow Networks
181. NeurIPS 2025Multidimensional Bayesian Utility Maximization: Tight Approximations to Welfare
182. NeurIPS 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
183. NeurIPS 2025One Filters All: A Generalist Filter For State Estimation
184. NeurIPS 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
185. NeurIPS 2025Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
186. NeurIPS 2025PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation
187. NeurIPS 2025Quantifying Uncertainty in the Presence of Distribution Shifts
188. NeurIPS 2025Squared families are useful conjugate priors
189. NeurIPS 2025Statistical Analysis of an Adversarial Bayesian Weak Supervision Method
190. NeurIPS 2025The Quotient Bayesian Learning Rule
191. NeurIPS 2025Torch-Uncertainty: Deep Learning Uncertainty Quantification
192. NeurIPS 2025Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
193. NeurIPS 2025TreeGen: A Bayesian Generative Model for Hierarchies
194. NeurIPS 2025Uncertainty Quantification with the Empirical Neural Tangent Kernel
195. NeurIPS 2025Uncertainty-Sensitive Privileged Learning
196. NeurIPS 2025VIKING: Deep variational inference with stochastic projections
197. CVPR 2024Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
198. CVPR 2024Bayesian Differentiable Physics for Cloth Digitalization
199. CVPR 2024Bayesian Diffusion Models for 3D Shape Reconstruction
200. CVPR 2024Epistemic Uncertainty Quantification For Pre-Trained Neural Networks

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.