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

139 papers, page 2 of 3

51. ICML 2026Robust Bayes-Assisted Conformal Prediction
52. ICML 2026Set-Preserving Calibration from Conformal P-Values to E-Values
53. ICML 2026Simultaneous Confidence Bounds for Aggregated Effects via Exact Subset Optimization
54. ICML 2026Spectral Flow Matching: Stabilizing Stochastic GFlowNets via Frequency-Domain Regularization
55. ICML 2026SpeedCP: Fast Kernel-based Conditional Conformal Prediction
56. ICML 2026Split Group Knockoffs: Controlling False Discovery Rate in Transformational Group Sparsity
57. ICML 2026SVRG and Beyond via Posterior Correction
58. ICML 2026Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
59. ICML 2026Transformers Can Learn Posterior Predictive Distributions In-Context
60. ICML 2025A Mixture-Based Framework for Guiding Diffusion Models
61. ICML 2025Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
62. ICML 2025Calibrated Value-Aware Model Learning with Probabilistic Environment Models
63. ICML 2025Can Transformers Learn Full Bayesian Inference in Context?
64. ICML 2025Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
65. ICML 2025Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention
66. ICML 2025Density Ratio Estimation with Conditional Probability Paths
67. ICML 2025Discrete Markov Probabilistic Models: An Improved Discrete Score-Based Framework with sharp convergence bounds under minimal assumptions
68. ICML 2025Doubly Robust Conformalized Survival Analysis with Right-Censored Data
69. ICML 2025Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity
70. ICML 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
71. ICML 2025Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
72. ICML 2025Gridded Transformer Neural Processes for Spatio-Temporal Data
73. ICML 2025Improving the Statistical Efficiency of Cross-Conformal Prediction
74. ICML 2025LEAPS: A discrete neural sampler via locally equivariant networks
75. ICML 2025Learning multivariate Gaussians with imperfect advice
76. ICML 2025Learning Survival Distributions with the Asymmetric Laplace Distribution
77. ICML 2025Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints
78. ICML 2025Polynomial-Delay MAG Listing with Novel Locally Complete Orientation Rules
79. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
80. ICML 2025Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
81. ICML 2025Prediction-Powered E-Values
82. ICML 2025Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning
83. ICML 2025Random Policy Evaluation Uncovers Policies of Generative Flow Networks
84. ICML 2025Rethinking Aleatoric and Epistemic Uncertainty
85. ICML 2025Robust Conformal Outlier Detection under Contaminated Reference Data
86. ICML 2025Sample Complexity of Branch-length Estimation by Maximum Likelihood
87. ICML 2025Scaling Probabilistic Circuits via Monarch Matrices
88. ICML 2025Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits
89. ICML 2025The Limits of Tractable Marginalization
90. ICML 2025To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
91. ICML 2025TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation
92. ICML 2025Trajectory Inference with Smooth Schrödinger Bridges
93. ICML 2025Variational Learning of Fractional Posteriors
94. ICML 2025Variational Phylogenetic Inference with Products over Bipartitions
95. ICML 2025Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
96. ICML 2025Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
97. ICML 2024A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
98. ICML 2024CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
99. ICML 2024Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
100. ICML 2024Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations

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.