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

107 papers, page 2 of 3

51. ICML 2026Learning to Bet for Horizon-Aware Anytime-Valid Testing
52. ICML 2026LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems
53. ICML 2026Maximum-Likelihood Learning of Latent Dynamics Without Reconstruction
54. ICML 2026Minimum Distance Summaries for Robust Neural Posterior Estimation
55. ICML 2026Monotonic Variational Gaussian Process for Efficient Data Collection
56. ICML 2026Multi-Distribution Robust Conformal Prediction
57. ICML 2026Multiple Choice Learning of Low-Rank Adapters for Language Modeling
58. ICML 2026Near-Universal Multiplicative Updates for Nonnegative Einsum Factorization
59. ICML 2026On the Epistemic Uncertainty of Overparametrized Neural Networks
60. ICML 2026Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
61. ICML 2026PPDL: LLM-Based Flows as Probabilistic Programs
62. ICML 2026Predictable Compression Failures: Order Sensitivity and Information Budgeting for Evidence-Grounded Binary Adjudication
63. ICML 2026Principled Confidence Estimation for Deep Computed Tomography
64. ICML 2026Probabilistic Bisection Algorithm Provably Achieves Exponential Convergence
65. ICML 2026Probabilistic Retrofitting of Learned Simulators
66. ICML 2026Probably Approximately Correct Labels
67. ICML 2026ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
68. ICML 2026Pseudo-Mallows for Efficient Probabilistic Preference Learning
69. ICML 2026Reduction of Probabilistic Chemical Reaction Networks
70. ICML 2026Revisiting Neural Processes via Fourier Transform and Volterra Series
71. ICML 2026Robust Bayes-Assisted Conformal Prediction
72. ICML 2026Set-Preserving Calibration from Conformal P-Values to E-Values
73. ICML 2026Simultaneous Confidence Bounds for Aggregated Effects via Exact Subset Optimization
74. ICML 2026Spectral Flow Matching: Stabilizing Stochastic GFlowNets via Frequency-Domain Regularization
75. ICML 2026SpeedCP: Fast Kernel-based Conditional Conformal Prediction
76. ICML 2026Split Group Knockoffs: Controlling False Discovery Rate in Transformational Group Sparsity
77. ICML 2026SVRG and Beyond via Posterior Correction
78. ICML 2026Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
79. ICML 2026Transformers Can Learn Posterior Predictive Distributions In-Context
80. NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
81. NeurIPS 2026A probabilistic model of visual segmentation explains early visual cortical dynamics
82. NeurIPS 2026Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
83. NeurIPS 2026Announced Breaks Separate Conformal Reliability from Frequency Calibration
84. NeurIPS 2026Auditing Conformal Prediction for Language Models Under Inference-Time Model Shift
85. NeurIPS 2026CoTs as Probabilistic Programs: A Programmatic View of Thinking Step-by-Step in Language Models
86. NeurIPS 2026Efficient Adaptive Data Acquisition via Pretrained Belief Representations
87. NeurIPS 2026Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
88. NeurIPS 2026Filtered-Trace Online Variational Training for Probabilistic Spiking Neural Networks
89. NeurIPS 2026FlowBatt: Flow Matching for Probabilistic Battery Degradation Prediction
90. NeurIPS 2026Heterogeneous Judge-Aware Ranking with Sensitivity, Disagreement, and Confidence
91. NeurIPS 2026Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects
92. NeurIPS 2026Inference and estimation with unidentifiable latent treatment effects
93. NeurIPS 2026Just Ramp-Up: Debiasing Regression-based Estimator for A/B Tests under Network Interference
94. NeurIPS 2026One-Shot Generative Flows: Existence and Obstructions
95. NeurIPS 2026Probabilistic Recursive Reasoning
96. NeurIPS 2026Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signatures
97. NeurIPS 2026Probabilistic Tiny Recursive Model
98. NeurIPS 2026probly: Uncertainty-Aware Machine Learning
99. NeurIPS 2026Robust Conditional Conformal Prediction via Branched Normalizing Flow
100. NeurIPS 2026Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters

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.