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

25 papers, page 1 of 1

1. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
2. ICML 2023A theory of representation learning gives a deep generalisation of kernel methods
3. ICML 2023A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
4. ICML 2023Automatically marginalized MCMC in probabilistic programming
5. ICML 2023Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
6. ICML 2023Correcting discount-factor mismatch in on-policy policy gradient methods
7. ICML 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
8. ICML 2023Direct Parameterization of Lipschitz-Bounded Deep Networks
9. ICML 2023FARE: Provably Fair Representation Learning with Practical Certificates
10. ICML 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspective
11. ICML 2023Learning Instance-Specific Augmentations by Capturing Local Invariances
12. ICML 2023Nearly-tight Bounds for Deep Kernel Learning
13. ICML 2023Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits
14. ICML 2023OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
15. ICML 2023On the Convergence Rates of Policy Gradient Methods
16. ICML 2023Principled Acceleration of Iterative Numerical Methods Using Machine Learning
17. ICML 2023Probabilistic Attention-to-Influence Neural Models for Event Sequences
18. ICML 2023Probabilistic Categorical Adversarial Attack and Adversarial Training
19. ICML 2023Probabilistic Concept Bottleneck Models
20. ICML 2023Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
21. ICML 2023Probabilistic Imputation for Time-series Classification with Missing Data
22. ICML 2023Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
23. ICML 2023Quantile Credit Assignment
24. ICML 2023Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
25. ICML 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees

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.