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

212 papers, page 2 of 5

51. NeurIPS 2025Deep Continuous-Time State-Space Models for Marked Event Sequences
52. NeurIPS 2025Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery
53. NeurIPS 2025Density Ratio-Free Doubly Robust Proxy Causal Learning
54. NeurIPS 2025Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference
55. NeurIPS 2025Differentiable Constraint-Based Causal Discovery
56. NeurIPS 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
57. NeurIPS 2025Differentiable Structure Learning and Causal Discovery for General Binary Data
58. NeurIPS 2025Diffusion Federated Dataset
59. NeurIPS 2025Direct Fisher Score Estimation for Likelihood Maximization
60. NeurIPS 2025Discrete Neural Flow Samplers with Locally Equivariant Transformer
61. NeurIPS 2025Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
62. NeurIPS 2025Distance-informed Neural Processes
63. NeurIPS 2025Distributed mediation analysis with communication efficiency
64. NeurIPS 2025Diverse Influence Component Analysis: A Geometric Approach to Nonlinear Mixture Identifiability
65. NeurIPS 2025Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the Role of Model Complexity
66. NeurIPS 2025Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
67. NeurIPS 2025DSCS: Fast CPDAG-Based Verification of Collapsible Submodels in High-Dimensional Bayesian Networks
68. NeurIPS 2025Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
69. NeurIPS 2025Edit Flows: Variable Length Discrete Flow Matching with Sequence-Level Edit Operations
70. NeurIPS 2025Efficient Adaptive Experimentation with Noncompliance
71. NeurIPS 2025Efficient Randomized Experiments Using Foundation Models
72. NeurIPS 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
73. NeurIPS 2025Embeddings as Probabilistic Equivalence in Logic Programs
74. NeurIPS 2025Energy-based generator matching: A neural sampler for general state space
75. NeurIPS 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
76. NeurIPS 2025EUGens: Efficient, Unified and General Dense Layers
77. NeurIPS 2025Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere
78. NeurIPS 2025Exploring and Exploiting Model Uncertainty in Bayesian Optimization
79. NeurIPS 2025Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data
80. NeurIPS 2025Fast Non-Log-Concave Sampling under Nonconvex Equality and Inequality Constraints with Landing
81. NeurIPS 2025Faster Generic Identification in Tree-Shaped Structural Causal Models
82. NeurIPS 2025FEAT: Free energy Estimators with Adaptive Transport
83. NeurIPS 2025Fisher meets Feynman: score-based variational inference with a product of experts
84. NeurIPS 2025Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises
85. NeurIPS 2025FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators
86. NeurIPS 2025From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling
87. NeurIPS 2025Functional Complexity-adaptive Temporal Tensor Decomposition
88. NeurIPS 2025Gaussian Processes for Shuffled Regression
89. NeurIPS 2025GMM-based VAE model with Normalising Flow for effective stochastic segmentation
90. NeurIPS 2025Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks
91. NeurIPS 2025GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
92. NeurIPS 2025Handling Missing Responses under Cluster Dependence with Applications to Language Model Evaluation
93. NeurIPS 2025HoT-VI: Reparameterizable Variational Inference for Capturing Instance-Level High-Order Correlations
94. NeurIPS 2025Identifying Macro Causal Effects in C-DMGs over DMGs
95. NeurIPS 2025Image Super-Resolution with Guarantees via Conformalized Generative Models
96. NeurIPS 2025Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
97. NeurIPS 2025Infinite Neural Operators: Gaussian processes on functions
98. NeurIPS 2025Informed Initialization for Bayesian Optimization and Active Learning
99. NeurIPS 2025Integral Imprecise Probability Metrics
100. NeurIPS 2025Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language 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.