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 1 of 5

1. NeurIPS 2025$\text{G}^2\text{M}$: A Generalized Gaussian Mirror Method to Boost Feature Selection Power
2. NeurIPS 2025A Black-Box Debiasing Framework for Conditional Sampling
3. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
4. NeurIPS 2025A Counterfactual Semantics for Hybrid Dynamical Systems
5. NeurIPS 2025A Hierarchy of Graphical Models for Counterfactual Inferences
6. NeurIPS 2025A Latent Multilayer Graphical Model For Complex, Interdependent Systems
7. NeurIPS 2025A Unified Framework for the Transportability of Population-Level Causal Measures
8. NeurIPS 2025Abstract Counterfactuals for Language Model Agents
9. NeurIPS 2025Accelerating Feature Conformal Prediction via Taylor Approximation
10. NeurIPS 2025Adaptive Data-Borrowing for Improving Treatment Effect Estimation using External Controls
11. NeurIPS 2025Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
12. NeurIPS 2025Adjoint Schrödinger Bridge Sampler
13. NeurIPS 2025Agents Robust to Distribution Shifts Learn Causal World Models Even Under Mediation
14. NeurIPS 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
15. NeurIPS 2025Amortized Active Generation of Pareto Sets
16. NeurIPS 2025Amortized Variational Transdimensional Inference
17. NeurIPS 2025An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
18. NeurIPS 2025Asymptotically exact variational flows via involutive MCMC kernels
19. NeurIPS 2025Bayesian Concept Bottleneck Models with LLM Priors
20. NeurIPS 2025BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
21. NeurIPS 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliance
22. NeurIPS 2025Bi-Directional Communication-Efficient Stochastic FL via Remote Source Generation
23. NeurIPS 2025Brain-like Variational Inference
24. NeurIPS 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
25. NeurIPS 2025Causal Discovery over Clusters of Variables in Markovian Systems
26. NeurIPS 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
27. NeurIPS 2025Certifying Deep Network Risks and Individual Predictions with PAC-Bayes Loss via Localized Priors
28. NeurIPS 2025Channel Simulation and Distributed Compression with Ensemble Rejection Sampling
29. NeurIPS 2025Characterization and Learning of Causal Graphs from Hard Interventions
30. NeurIPS 2025Collaborative and Confidential Junction Trees for Hybrid Bayesian Networks
31. NeurIPS 2025CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates
32. NeurIPS 2025Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
33. NeurIPS 2025Conformal Prediction for Causal Effects of Continuous Treatments
34. NeurIPS 2025Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
35. NeurIPS 2025Conformal Prediction for Time-series Forecasting with Change Points
36. NeurIPS 2025Conformal Prediction under Lévy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations
37. NeurIPS 2025Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
38. NeurIPS 2025Confounding Robust Deep Reinforcement Learning: A Causal Approach
39. NeurIPS 2025Connecting Jensen–Shannon and Kullback–Leibler Divergences: A New Bound for Representation Learning
40. NeurIPS 2025Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective
41. NeurIPS 2025Counterfactual Image Editing with Disentangled Causal Latent Space
42. NeurIPS 2025Counterfactual Implicit Feedback Modeling
43. NeurIPS 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridge
44. NeurIPS 2025Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
45. NeurIPS 2025Credal Prediction based on Relative Likelihood
46. NeurIPS 2025Curious Causality-Seeking Agents Learn Meta Causal World
47. NeurIPS 2025Data Fusion for Partial Identification of Causal Effects
48. NeurIPS 2025Data-Adaptive Exposure Thresholds under Network Interference
49. NeurIPS 2025DeCaFlow: A deconfounding causal generative model
50. NeurIPS 2025Decomposing Interventional Causality into Synergistic, Redundant, and Unique Components

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.