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

69 papers, page 1 of 2

1. NeurIPS 2023$L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting
2. NeurIPS 2023A Computationally Efficient Sparsified Online Newton Method
3. NeurIPS 2023A Heavy-Tailed Algebra for Probabilistic Programming
4. NeurIPS 2023A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
5. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
6. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
7. NeurIPS 2023Block Broyden's Methods for Solving Nonlinear Equations
8. NeurIPS 2023Byzantine-Tolerant Methods for Distributed Variational Inequalities
9. NeurIPS 2023Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
10. NeurIPS 2023Characteristic Circuits
11. NeurIPS 2023CORL: Research-oriented Deep Offline Reinforcement Learning Library
12. NeurIPS 2023Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
13. NeurIPS 2023Credal Marginal MAP
14. NeurIPS 2023Deep Fractional Fourier Transform
15. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
16. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
17. NeurIPS 2023Deep Recurrent Optimal Stopping
18. NeurIPS 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
19. NeurIPS 2023Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation
20. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
21. NeurIPS 2023Faster Differentially Private Convex Optimization via Second-Order Methods
22. NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
23. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
24. NeurIPS 2023How a Student becomes a Teacher: learning and forgetting through Spectral methods
25. NeurIPS 2023How many samples are needed to leverage smoothness?
26. NeurIPS 2023Human spatiotemporal pattern learning as probabilistic program synthesis
27. NeurIPS 2023Imitation Learning from Vague Feedback
28. NeurIPS 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
29. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
30. NeurIPS 2023Jigsaw: Learning to Assemble Multiple Fractured Objects
31. NeurIPS 2023Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
32. NeurIPS 2023Learning Functional Transduction
33. NeurIPS 2023Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
34. NeurIPS 2023Learning Re-sampling Methods with Parameter Attribution for Image Super-resolution
35. NeurIPS 2023Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
36. NeurIPS 2023MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
37. NeurIPS 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
38. NeurIPS 2023Non-autoregressive Machine Translation with Probabilistic Context-free Grammar
39. NeurIPS 2023Non-Rigid Shape Registration via Deep Functional Maps Prior
40. NeurIPS 2023On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
41. NeurIPS 2023Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games
42. NeurIPS 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
43. NeurIPS 2023Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
44. NeurIPS 2023Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
45. NeurIPS 2023Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
46. NeurIPS 2023Probabilistic Exponential Integrators
47. NeurIPS 2023Probabilistic Inference in Reinforcement Learning Done Right
48. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
49. NeurIPS 2023Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems
50. NeurIPS 2023PROTES: Probabilistic Optimization with Tensor Sampling

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.