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

131 papers, page 2 of 3

51. NeurIPS 2024Fearless Stochasticity in Expectation Propagation
52. NeurIPS 2024Foundation Inference Models for Markov Jump Processes
53. NeurIPS 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
54. NeurIPS 2024Functional Gradient Flows for Constrained Sampling
55. NeurIPS 2024Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
56. NeurIPS 2024Gaussian Process Bandits for Top-k Recommendations
57. NeurIPS 2024General bounds on the quality of Bayesian coresets
58. NeurIPS 2024Generative Adversarial Model-Based Optimization via Source Critic Regularization
59. NeurIPS 2024Globally Convergent Variational Inference
60. NeurIPS 2024Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
61. NeurIPS 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
62. NeurIPS 2024Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
63. NeurIPS 2024Hierarchical Uncertainty Exploration via Feedforward Posterior Trees
64. NeurIPS 2024High Rank Path Development: an approach to learning the filtration of stochastic processes
65. NeurIPS 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
66. NeurIPS 2024Hyper-opinion Evidential Deep Learning for Out-of-Distribution Detection
67. NeurIPS 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
68. NeurIPS 2024Improved off-policy training of diffusion samplers
69. NeurIPS 2024Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
70. NeurIPS 2024In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
71. NeurIPS 2024Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
72. NeurIPS 2024Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
73. NeurIPS 2024Interaction-Force Transport Gradient Flows
74. NeurIPS 2024Inversion-based Latent Bayesian Optimization
75. NeurIPS 2024Is Score Matching Suitable for Estimating Point Processes?
76. NeurIPS 2024Kermut: Composite kernel regression for protein variant effects
77. NeurIPS 2024Learning to be Smooth: An End-to-End Differentiable Particle Smoother
78. NeurIPS 2024Linear Uncertainty Quantification of Graphical Model Inference
79. NeurIPS 2024LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
80. NeurIPS 2024Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk
81. NeurIPS 2024Low Degree Hardness for Broadcasting on Trees
82. NeurIPS 2024Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
83. NeurIPS 2024Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
84. NeurIPS 2024Multivariate Probabilistic Time Series Forecasting with Correlated Errors
85. NeurIPS 2024Mutual Information Estimation via $f$-Divergence and Data Derangements
86. NeurIPS 2024Mutual Information Estimation via Normalizing Flows
87. NeurIPS 2024Noether's Razor: Learning Conserved Quantities
88. NeurIPS 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
89. NeurIPS 2024Nonstationary Sparse Spectral Permanental Process
90. NeurIPS 2024On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
91. NeurIPS 2024On the Expressive Power of Tree-Structured Probabilistic Circuits
92. NeurIPS 2024Particle Semi-Implicit Variational Inference
93. NeurIPS 2024Physics-Informed Variational State-Space Gaussian Processes
94. NeurIPS 2024Practical Bayesian Algorithm Execution via Posterior Sampling
95. NeurIPS 2024Practical Shuffle Coding
96. NeurIPS 2024Preferential Normalizing Flows
97. NeurIPS 2024Principled Bayesian Optimization in Collaboration with Human Experts
98. NeurIPS 2024Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
99. NeurIPS 2024Probabilistic Graph Rewiring via Virtual Nodes
100. NeurIPS 2024Probabilistic size-and-shape functional mixed models

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.