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

440 papers, page 1 of 9

1. NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
2. NeurIPS 2026A probabilistic model of visual segmentation explains early visual cortical dynamics
3. NeurIPS 2026Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
4. NeurIPS 2026Announced Breaks Separate Conformal Reliability from Frequency Calibration
5. NeurIPS 2026Auditing Conformal Prediction for Language Models Under Inference-Time Model Shift
6. NeurIPS 2026CoTs as Probabilistic Programs: A Programmatic View of Thinking Step-by-Step in Language Models
7. NeurIPS 2026Efficient Adaptive Data Acquisition via Pretrained Belief Representations
8. NeurIPS 2026Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
9. NeurIPS 2026Filtered-Trace Online Variational Training for Probabilistic Spiking Neural Networks
10. NeurIPS 2026FlowBatt: Flow Matching for Probabilistic Battery Degradation Prediction
11. NeurIPS 2026Heterogeneous Judge-Aware Ranking with Sensitivity, Disagreement, and Confidence
12. NeurIPS 2026Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects
13. NeurIPS 2026Inference and estimation with unidentifiable latent treatment effects
14. NeurIPS 2026Just Ramp-Up: Debiasing Regression-based Estimator for A/B Tests under Network Interference
15. NeurIPS 2026One-Shot Generative Flows: Existence and Obstructions
16. NeurIPS 2026Probabilistic Recursive Reasoning
17. NeurIPS 2026Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signatures
18. NeurIPS 2026Probabilistic Tiny Recursive Model
19. NeurIPS 2026probly: Uncertainty-Aware Machine Learning
20. NeurIPS 2026Robust Conditional Conformal Prediction via Branched Normalizing Flow
21. NeurIPS 2026Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters
22. NeurIPS 2026Score-based Variational Inference via Quantum Maximally Mixed States
23. NeurIPS 2026Sliced Inner Product Gromov–Wasserstein Distances
24. NeurIPS 2026Stabilizing Few-Shot Object Detection with Language-Conditioned Probabilistic Prototypes
25. NeurIPS 2026Stable GFlowNets with Probabilistic Guarantees
26. NeurIPS 2026The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
27. NeurIPS 2026Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision
28. NeurIPS 2026Where to Approximate in Neurosymbolic Inference?
29. NeurIPS 2025$\text{G}^2\text{M}$: A Generalized Gaussian Mirror Method to Boost Feature Selection Power
30. NeurIPS 2025A Black-Box Debiasing Framework for Conditional Sampling
31. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
32. NeurIPS 2025A Counterfactual Semantics for Hybrid Dynamical Systems
33. NeurIPS 2025A Hierarchy of Graphical Models for Counterfactual Inferences
34. NeurIPS 2025A Latent Multilayer Graphical Model For Complex, Interdependent Systems
35. NeurIPS 2025A Unified Framework for the Transportability of Population-Level Causal Measures
36. NeurIPS 2025Abstract Counterfactuals for Language Model Agents
37. NeurIPS 2025Accelerating Feature Conformal Prediction via Taylor Approximation
38. NeurIPS 2025Adaptive Data-Borrowing for Improving Treatment Effect Estimation using External Controls
39. NeurIPS 2025Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
40. NeurIPS 2025Adjoint Schrödinger Bridge Sampler
41. NeurIPS 2025Agents Robust to Distribution Shifts Learn Causal World Models Even Under Mediation
42. NeurIPS 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
43. NeurIPS 2025Amortized Active Generation of Pareto Sets
44. NeurIPS 2025Amortized Variational Transdimensional Inference
45. NeurIPS 2025An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
46. NeurIPS 2025Asymptotically exact variational flows via involutive MCMC kernels
47. NeurIPS 2025Bayesian Concept Bottleneck Models with LLM Priors
48. NeurIPS 2025BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
49. NeurIPS 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliance
50. NeurIPS 2025Bi-Directional Communication-Efficient Stochastic FL via Remote Source Generation

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.