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 4 of 9

151. NeurIPS 2025Multivariate Latent Recalibration for Conditional Normalizing Flows
152. NeurIPS 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
153. NeurIPS 2025Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
154. NeurIPS 2025Neural Hamiltonian Diffusions for Modeling Structured Geometric Dynamics
155. NeurIPS 2025Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
156. NeurIPS 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
157. NeurIPS 2025Neurosymbolic Diffusion Models
158. NeurIPS 2025NeuSymEA: Neuro-symbolic Entity Alignment via Variational Inference
159. NeurIPS 2025Non-equilibrium Annealed Adjoint Sampler
160. NeurIPS 2025Non-exchangeable Conformal Prediction with Optimal Transport: Tackling Distribution Shift with Unlabeled Data
161. NeurIPS 2025Non-Stationary Structural Causal Bandits
162. NeurIPS 2025Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
163. NeurIPS 2025On scalable and efficient training of diffusion samplers
164. NeurIPS 2025On the Hardness of Approximating Distributions with Tractable Probabilistic Models
165. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
166. NeurIPS 2025One Sample is Enough to Make Conformal Prediction Robust
167. NeurIPS 2025Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
168. NeurIPS 2025Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect
169. NeurIPS 2025Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
170. NeurIPS 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
171. NeurIPS 2025Parallelizing MCMC Across the Sequence Length
172. NeurIPS 2025Personalized Federated Conformal Prediction with Localization
173. NeurIPS 2025PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation
174. NeurIPS 2025Practical Kernel Selection for Kernel-based Conditional Independence Test
175. NeurIPS 2025Prediction-Powered Causal Inferences
176. NeurIPS 2025Probabilistic Token Alignment for Large Language Model Fusion
177. NeurIPS 2025ProDAG: Projected Variational Inference for Directed Acyclic Graphs
178. NeurIPS 2025PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units
179. NeurIPS 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference
180. NeurIPS 2025Quantifying Uncertainty in the Presence of Distribution Shifts
181. NeurIPS 2025Quantitative convergence of trained neural networks to Gaussian processes
182. NeurIPS 2025Quantum speedup of non-linear Monte Carlo problems
183. NeurIPS 2025Rao-Blackwellised Reparameterisation Gradients
184. NeurIPS 2025Recurrent Memory for Online Interdomain Gaussian Processes
185. NeurIPS 2025Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference
186. NeurIPS 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
187. NeurIPS 2025Resolution of Simpson's paradox via the common cause principle
188. NeurIPS 2025Rethinking Approximate Gaussian Inference in Classification
189. NeurIPS 2025Rethinking Losses for Diffusion Bridge Samplers
190. NeurIPS 2025Reverse Diffusion Sequential Monte Carlo Samplers
191. NeurIPS 2025Reverse-Annealed Sequential Monte Carlo for Efficient Bayesian Optimal Experiment Design
192. NeurIPS 2025Reward-oriented Causal Representation Learning
193. NeurIPS 2025Robust and Computation-Aware Gaussian Processes
194. NeurIPS 2025Robust Sampling for Active Statistical Inference
195. NeurIPS 2025Root Cause Analysis of Outliers with Missing Structural Knowledge
196. NeurIPS 2025Sampling by averaging: A multiscale approach to score estimation
197. NeurIPS 2025Scalable and adaptive prediction bands with kernel sum-of-squares
198. NeurIPS 2025Score-Based Diffusion Modeling for Nonparametric Empirical Bayes in Heteroscedastic Gaussian Mixtures
199. NeurIPS 2025Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
200. NeurIPS 2025SD-KDE: Score-Debiased Kernel Density Estimation

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.