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 3 of 5

101. NeurIPS 2025Large Language Bayes
102. NeurIPS 2025Learning Counterfactual Outcomes Under Rank Preservation
103. NeurIPS 2025Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
104. NeurIPS 2025Learning normalized image densities via dual score matching
105. NeurIPS 2025Learning to Condition: A Neural Heuristic for Scalable MPE Inference
106. NeurIPS 2025Least squares variational inference
107. NeurIPS 2025Less Greedy Equivalence Search
108. NeurIPS 2025Leveraging semantic similarity for experimentation with AI-generated treatments
109. NeurIPS 2025List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression
110. NeurIPS 2025Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
111. NeurIPS 2025Low-Rank Graphon Learning for Networks
112. NeurIPS 2025Martingale Posterior Neural Networks for Fast Sequential Decision Making
113. NeurIPS 2025Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise
114. NeurIPS 2025MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
115. NeurIPS 2025Measure-Theoretic Anti-Causal Representation Learning
116. NeurIPS 2025Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo
117. NeurIPS 2025MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions
118. NeurIPS 2025Model Reconciliation via Cost-Optimal Explanations in Probabilistic Logic Programming
119. NeurIPS 2025Model-Informed Flows for Bayesian Inference
120. NeurIPS 2025Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability Graphs
121. NeurIPS 2025Multi-View Oriented GPLVM: Expressiveness and Efficiency
122. NeurIPS 2025Multilevel neural simulation-based inference
123. NeurIPS 2025Multivariate Latent Recalibration for Conditional Normalizing Flows
124. NeurIPS 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
125. NeurIPS 2025Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
126. NeurIPS 2025Neural Hamiltonian Diffusions for Modeling Structured Geometric Dynamics
127. NeurIPS 2025Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
128. NeurIPS 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
129. NeurIPS 2025Neurosymbolic Diffusion Models
130. NeurIPS 2025NeuSymEA: Neuro-symbolic Entity Alignment via Variational Inference
131. NeurIPS 2025Non-equilibrium Annealed Adjoint Sampler
132. NeurIPS 2025Non-exchangeable Conformal Prediction with Optimal Transport: Tackling Distribution Shift with Unlabeled Data
133. NeurIPS 2025Non-Stationary Structural Causal Bandits
134. NeurIPS 2025Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
135. NeurIPS 2025On scalable and efficient training of diffusion samplers
136. NeurIPS 2025On the Hardness of Approximating Distributions with Tractable Probabilistic Models
137. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
138. NeurIPS 2025One Sample is Enough to Make Conformal Prediction Robust
139. NeurIPS 2025Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
140. NeurIPS 2025Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect
141. NeurIPS 2025Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
142. NeurIPS 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
143. NeurIPS 2025Parallelizing MCMC Across the Sequence Length
144. NeurIPS 2025Personalized Federated Conformal Prediction with Localization
145. NeurIPS 2025PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation
146. NeurIPS 2025Practical Kernel Selection for Kernel-based Conditional Independence Test
147. NeurIPS 2025Prediction-Powered Causal Inferences
148. NeurIPS 2025Probabilistic Token Alignment for Large Language Model Fusion
149. NeurIPS 2025ProDAG: Projected Variational Inference for Directed Acyclic Graphs
150. NeurIPS 2025PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units

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.