Deep learning · probabilistic methods papers, 2023–2026

Key findings

• Deep learning · probabilistic methods went from 107 papers in 2023 to 107 in 2026, roughly 1.0x in three years.
• NeurIPS publishes the most Deep learning · probabilistic methods work: 440 papers from 2023 to 2026, 69% of the four-venue total.
• The fastest-growing overlap is with Simulation for Driving: papers tagged with both rose from 5 in 2023 to 14 in 2026.
• Among the 11 topics under Deep Learning, it ranks #9 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
• ICML 2026An Efficient Joint Learning Approach for Item Response Theory
• ICLR 2026Probabilistic Kernel Function for Fast Angle Testing
• CVPR 2026Beyond Text Visual Description Assembly by Probabilistic Model for CLIP-based
• NeurIPS 2025$\text{G}^2\text{M}$: A Generalized Gaussian Mirror Method to Boost Feature Selection Power
• ICML 2025Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
• ICLR 2025P-SPIKESSM: HARNESSING PROBABILISTIC SPIKING STATE SPACE MODELS FOR LONG-RANGE DEPENDENCY TASKS
• CVPR 2025ProbPose: A Probabilistic Approach to 2D Human Pose Estimation
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
• ICLR 2024A Probabilistic Framework for Modular Continual Learning
• CVPR 2024CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
• NeurIPS 2023A Computationally Efficient Sparsified Online Newton Method
• ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
• ICLR 2023A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation
• CVPR 2023Probabilistic Knowledge Distillation of Face Ensembles
• NeurIPS 2026Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
• ICML 2026Avoid What You Know: Divergent Trajectory Balance for GFlowNets
• ICLR 2026Learning Survival Distributions with Individually Calibrated Asymmetric Laplace Distribution
• CVPR 2026ARES Unifying Asymmetric RGB-Event Stereo for Probabilistic Scene Flow Estimation

All Deep learning · probabilistic methods papers

160 papers, page 2 of 4

51. NeurIPS 2024Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments
52. NeurIPS 2024Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing
53. NeurIPS 2024Bayesian Online Natural Gradient (BONG)
54. NeurIPS 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
55. NeurIPS 2024BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
56. NeurIPS 2024Block Sparse Bayesian Learning: A Diversified Scheme
57. NeurIPS 2024BMRS: Bayesian Model Reduction for Structured Pruning
58. NeurIPS 2024Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models
59. NeurIPS 2024Community Detection Guarantees using Embeddings Learned by Node2Vec
60. NeurIPS 2024Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
61. NeurIPS 2024Conditional Density Estimation with Histogram Trees
62. NeurIPS 2024Conformalized Credal Set Predictors
63. NeurIPS 2024Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
64. NeurIPS 2024Consistency Models for Scalable and Fast Simulation-Based Inference
65. NeurIPS 2024Constrained Binary Decision Making
66. NeurIPS 2024Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
67. NeurIPS 2024Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
68. NeurIPS 2024Credal Deep Ensembles for Uncertainty Quantification
69. NeurIPS 2024Decomposable Transformer Point Processes
70. NeurIPS 2024Deep Learning for Computing Convergence Rates of Markov Chains
71. NeurIPS 2024Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors
72. NeurIPS 2024Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
73. NeurIPS 2024EigenVI: score-based variational inference with orthogonal function expansions
74. NeurIPS 2024Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
75. NeurIPS 2024Expected Probabilistic Hierarchies
76. NeurIPS 2024Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
77. NeurIPS 2024eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling
78. NeurIPS 2024FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings
79. NeurIPS 2024Fast Channel Simulation via Error-Correcting Codes
80. NeurIPS 2024Fearless Stochasticity in Expectation Propagation
81. NeurIPS 2024Foundation Inference Models for Markov Jump Processes
82. NeurIPS 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
83. NeurIPS 2024Functional Gradient Flows for Constrained Sampling
84. NeurIPS 2024Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
85. NeurIPS 2024Gaussian Process Bandits for Top-k Recommendations
86. NeurIPS 2024General bounds on the quality of Bayesian coresets
87. NeurIPS 2024Generative Adversarial Model-Based Optimization via Source Critic Regularization
88. NeurIPS 2024Globally Convergent Variational Inference
89. NeurIPS 2024Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
90. NeurIPS 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
91. NeurIPS 2024Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
92. NeurIPS 2024Hierarchical Uncertainty Exploration via Feedforward Posterior Trees
93. NeurIPS 2024High Rank Path Development: an approach to learning the filtration of stochastic processes
94. NeurIPS 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
95. NeurIPS 2024Hyper-opinion Evidential Deep Learning for Out-of-Distribution Detection
96. NeurIPS 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
97. NeurIPS 2024Improved off-policy training of diffusion samplers
98. NeurIPS 2024Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
99. NeurIPS 2024In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
100. NeurIPS 2024Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference

FAQ

How many Deep learning · probabilistic methods papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

637 in total: 107 in 2023, 160 in 2024, 263 in 2025, 107 in 2026.

How many Deep learning · probabilistic methods papers are at NeurIPS 2026?

NeurIPS 2026 has 28 Deep learning · probabilistic methods papers, versus 212 the year before.

How many Deep learning · probabilistic methods papers are at ICLR 2026?

ICLR 2026 has 13 Deep learning · probabilistic methods papers, versus 10 the year before.

How many Deep learning · probabilistic methods papers are at ICML 2026?

ICML 2026 has 59 Deep learning · probabilistic methods papers, versus 37 the year before.

How many Deep learning · probabilistic methods papers are at CVPR 2026?

CVPR 2026 has 7 Deep learning · probabilistic methods papers, versus 4 the year before.

Which conference publishes the most Deep learning · probabilistic methods papers?

NeurIPS, with 440 papers from 2023 to 2026.