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 2 of 9

51. NeurIPS 2025Brain-like Variational Inference
52. NeurIPS 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
53. NeurIPS 2025Causal Discovery over Clusters of Variables in Markovian Systems
54. NeurIPS 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
55. NeurIPS 2025Certifying Deep Network Risks and Individual Predictions with PAC-Bayes Loss via Localized Priors
56. NeurIPS 2025Channel Simulation and Distributed Compression with Ensemble Rejection Sampling
57. NeurIPS 2025Characterization and Learning of Causal Graphs from Hard Interventions
58. NeurIPS 2025Collaborative and Confidential Junction Trees for Hybrid Bayesian Networks
59. NeurIPS 2025CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates
60. NeurIPS 2025Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
61. NeurIPS 2025Conformal Prediction for Causal Effects of Continuous Treatments
62. NeurIPS 2025Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
63. NeurIPS 2025Conformal Prediction for Time-series Forecasting with Change Points
64. NeurIPS 2025Conformal Prediction under Lévy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations
65. NeurIPS 2025Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
66. NeurIPS 2025Confounding Robust Deep Reinforcement Learning: A Causal Approach
67. NeurIPS 2025Connecting Jensen–Shannon and Kullback–Leibler Divergences: A New Bound for Representation Learning
68. NeurIPS 2025Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective
69. NeurIPS 2025Counterfactual Image Editing with Disentangled Causal Latent Space
70. NeurIPS 2025Counterfactual Implicit Feedback Modeling
71. NeurIPS 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridge
72. NeurIPS 2025Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
73. NeurIPS 2025Credal Prediction based on Relative Likelihood
74. NeurIPS 2025Curious Causality-Seeking Agents Learn Meta Causal World
75. NeurIPS 2025Data Fusion for Partial Identification of Causal Effects
76. NeurIPS 2025Data-Adaptive Exposure Thresholds under Network Interference
77. NeurIPS 2025DeCaFlow: A deconfounding causal generative model
78. NeurIPS 2025Decomposing Interventional Causality into Synergistic, Redundant, and Unique Components
79. NeurIPS 2025Deep Continuous-Time State-Space Models for Marked Event Sequences
80. NeurIPS 2025Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery
81. NeurIPS 2025Density Ratio-Free Doubly Robust Proxy Causal Learning
82. NeurIPS 2025Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference
83. NeurIPS 2025Differentiable Constraint-Based Causal Discovery
84. NeurIPS 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
85. NeurIPS 2025Differentiable Structure Learning and Causal Discovery for General Binary Data
86. NeurIPS 2025Diffusion Federated Dataset
87. NeurIPS 2025Direct Fisher Score Estimation for Likelihood Maximization
88. NeurIPS 2025Discrete Neural Flow Samplers with Locally Equivariant Transformer
89. NeurIPS 2025Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
90. NeurIPS 2025Distance-informed Neural Processes
91. NeurIPS 2025Distributed mediation analysis with communication efficiency
92. NeurIPS 2025Diverse Influence Component Analysis: A Geometric Approach to Nonlinear Mixture Identifiability
93. NeurIPS 2025Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the Role of Model Complexity
94. NeurIPS 2025Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
95. NeurIPS 2025DSCS: Fast CPDAG-Based Verification of Collapsible Submodels in High-Dimensional Bayesian Networks
96. NeurIPS 2025Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
97. NeurIPS 2025Edit Flows: Variable Length Discrete Flow Matching with Sequence-Level Edit Operations
98. NeurIPS 2025Efficient Adaptive Experimentation with Noncompliance
99. NeurIPS 2025Efficient Randomized Experiments Using Foundation Models
100. NeurIPS 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models

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.