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

121 papers, page 2 of 3

51. ICML 2025FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
52. ICML 2025GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras
53. ICML 2025Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference
54. ICML 2025Improving Generalization with Flat Hilbert Bayesian Inference
55. ICML 2025Latent Variable Estimation in Bayesian Black-Litterman Models
56. ICML 2025LEAPS: A discrete neural sampler via locally equivariant networks
57. ICML 2025Learning Bayesian Nash Equilibrium in Auction Games via Approximate Best Response
58. ICML 2025Learning Gaussian DAG Models without Condition Number Bounds
59. ICML 2025Linearization Turns Neural Operators into Function-Valued Gaussian Processes
60. ICML 2025Maximizing Intermediate Checkpoint Value in LLM Pretraining with Bayesian Optimization
61. ICML 2025Models of Heavy-Tailed Mechanistic Universality
62. ICML 2025Position: The Future of Bayesian Prediction Is Prior-Fitted
63. ICML 2025Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
64. ICML 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
65. ICML 2025Scalable Gaussian Processes with Latent Kronecker Structure
66. ICML 2024A Bayesian Approach to Online Planning
67. ICML 2024A Rate-Distortion View of Uncertainty Quantification
68. ICML 2024A2Q+: Improving Accumulator-Aware Weight Quantization
69. ICML 2024Accelerating Convergence in Bayesian Few-Shot Classification
70. ICML 2024Bayesian Exploration Networks
71. ICML 2024Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification
72. ICML 2024Bayesian Program Learning by Decompiling Amortized Knowledge
73. ICML 2024Bayesian Regret Minimization in Offline Bandits
74. ICML 2024Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
75. ICML 2024BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
76. ICML 2024Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
77. ICML 2024Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
78. ICML 2024Enabling Uncertainty Estimation in Iterative Neural Networks
79. ICML 2024Equivariant Deep Weight Space Alignment
80. ICML 2024High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
81. ICML 2024Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
82. ICML 2024Improving Neural Additive Models with Bayesian Principles
83. ICML 2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
84. ICML 2024Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
85. ICML 2024Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
86. ICML 2024Learning to Continually Learn with the Bayesian Principle
87. ICML 2024MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
88. ICML 2024More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms
89. ICML 2024On the Weight Dynamics of Deep Normalized Networks
90. ICML 2024PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models
91. ICML 2024PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
92. ICML 2024Partially Stochastic Infinitely Deep Bayesian Neural Networks
93. ICML 2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
94. ICML 2024Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
95. ICML 2024Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks
96. ICML 2024Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks
97. ICML 2024Second-Order Uncertainty Quantification: A Distance-Based Approach
98. ICML 2024The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks
99. ICML 2024Transitional Uncertainty with Layered Intermediate Predictions
100. ICML 2024Uncertainty Estimation by Density Aware Evidential Deep Learning

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.