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 5 of 9

201. CVPR 2024Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models
202. CVPR 2024Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture
203. CVPR 2024Uncertainty Visualization via Low-Dimensional Posterior Projections
204. ICLR 2024A Hierarchical Bayesian Model for Few-Shot Meta Learning
205. ICLR 2024A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
206. ICLR 2024A Unified Framework for Bayesian Optimization under Contextual Uncertainty
207. ICLR 2024Adversarial Causal Bayesian Optimization
208. ICLR 2024BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
209. ICLR 2024Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures
210. ICLR 2024Bayesian Low-rank Adaptation for Large Language Models
211. ICLR 2024Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
212. ICLR 2024Conformal Risk Control
213. ICLR 2024Convolutional Deep Kernel Machines
214. ICLR 2024Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
215. ICLR 2024Entropy-MCMC: Sampling from Flat Basins with Ease
216. ICLR 2024Fast Ensembling with Diffusion Schrödinger Bridge
217. ICLR 2024Function-space Parameterization of Neural Networks for Sequential Learning
218. ICLR 2024Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
219. ICLR 2024Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning
220. ICLR 2024In-Context Learning through the Bayesian Prism
221. ICLR 2024Learning model uncertainty as variance-minimizing instance weights
222. ICLR 2024Leveraging Uncertainty Estimates To Improve Classifier Performance
223. ICLR 2024Non-Exchangeable Conformal Risk Control
224. ICLR 2024PAC Prediction Sets Under Label Shift
225. ICLR 2024R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
226. ICLR 2024Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
227. ICLR 2024Training Bayesian Neural Networks with Sparse Subspace Variational Inference
228. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
229. ICLR 2024Variational Bayesian Last Layers
230. ICML 2024A Bayesian Approach to Online Planning
231. ICML 2024A Rate-Distortion View of Uncertainty Quantification
232. ICML 2024A2Q+: Improving Accumulator-Aware Weight Quantization
233. ICML 2024Accelerating Convergence in Bayesian Few-Shot Classification
234. ICML 2024Bayesian Exploration Networks
235. ICML 2024Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
236. ICML 2024Bayesian Program Learning by Decompiling Amortized Knowledge
237. ICML 2024Bayesian Regret Minimization in Offline Bandits
238. ICML 2024Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
239. ICML 2024BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
240. ICML 2024Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
241. ICML 2024Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
242. ICML 2024Enabling Uncertainty Estimation in Iterative Neural Networks
243. ICML 2024Equivariant Deep Weight Space Alignment
244. ICML 2024High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
245. ICML 2024Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
246. ICML 2024Improving Neural Additive Models with Bayesian Principles
247. ICML 2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
248. ICML 2024Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
249. ICML 2024Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
250. ICML 2024Learning to Continually Learn with the Bayesian Principle

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.