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

263 papers, page 5 of 6

201. NeurIPS 2025PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units
202. NeurIPS 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference
203. NeurIPS 2025Quantifying Uncertainty in the Presence of Distribution Shifts
204. NeurIPS 2025Quantitative convergence of trained neural networks to Gaussian processes
205. NeurIPS 2025Quantum speedup of non-linear Monte Carlo problems
206. NeurIPS 2025Rao-Blackwellised Reparameterisation Gradients
207. NeurIPS 2025Recurrent Memory for Online Interdomain Gaussian Processes
208. NeurIPS 2025Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference
209. NeurIPS 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
210. NeurIPS 2025Resolution of Simpson's paradox via the common cause principle
211. NeurIPS 2025Rethinking Approximate Gaussian Inference in Classification
212. NeurIPS 2025Rethinking Losses for Diffusion Bridge Samplers
213. NeurIPS 2025Reverse Diffusion Sequential Monte Carlo Samplers
214. NeurIPS 2025Reverse-Annealed Sequential Monte Carlo for Efficient Bayesian Optimal Experiment Design
215. NeurIPS 2025Reward-oriented Causal Representation Learning
216. NeurIPS 2025Robust and Computation-Aware Gaussian Processes
217. NeurIPS 2025Robust Sampling for Active Statistical Inference
218. NeurIPS 2025Root Cause Analysis of Outliers with Missing Structural Knowledge
219. NeurIPS 2025Sampling by averaging: A multiscale approach to score estimation
220. NeurIPS 2025Scalable and adaptive prediction bands with kernel sum-of-squares
221. NeurIPS 2025Score-Based Diffusion Modeling for Nonparametric Empirical Bayes in Heteroscedastic Gaussian Mixtures
222. NeurIPS 2025Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
223. NeurIPS 2025SD-KDE: Score-Debiased Kernel Density Estimation
224. NeurIPS 2025SimpleStrat: Diversifying Language Model Generation with Stratification
225. NeurIPS 2025Simulation-Based Inference for Adaptive Experiments
226. NeurIPS 2025SING: SDE Inference via Natural Gradients
227. NeurIPS 2025Small Resamples, Sharp Guarantees: Convergence Rates for Resampled Studentized Quantile Estimators
228. NeurIPS 2025Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Associations
229. NeurIPS 2025Solving and Learning Partial Differential Equations with Variational Q-Exponential Processes
230. NeurIPS 2025Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited
231. NeurIPS 2025Spectral Convolutional Conditional Neural Process
232. NeurIPS 2025Spectral Estimation with Free Decompression
233. NeurIPS 2025Split Gibbs Discrete Diffusion Posterior Sampling
234. NeurIPS 2025Squared families are useful conjugate priors
235. NeurIPS 2025STACI: Spatio-Temporal Aleatoric Conformal Inference
236. NeurIPS 2025Structural Causal Bandits under Markov Equivalence
237. NeurIPS 2025Synthetic-powered predictive inference
238. NeurIPS 2025Temporal-Difference Variational Continual Learning
239. NeurIPS 2025Test Time Scaling for Neural Processes
240. NeurIPS 2025The Adaptive Complexity of Minimizing Relative Fisher Information
241. NeurIPS 2025The Quotient Bayesian Learning Rule
242. NeurIPS 2025The third pillar of causal analysis? A measurement perspective on causal representations
243. NeurIPS 2025Towards Identifiability of Hierarchical Temporal Causal Representation Learning
244. NeurIPS 2025Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
245. NeurIPS 2025Transferring Causal Effects using Proxies
246. NeurIPS 2025Treatment Effect Estimation for Optimal Decision-Making
247. NeurIPS 2025Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference
248. NeurIPS 2025Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
249. NeurIPS 2025Uncertainty Quantification with the Empirical Neural Tangent Kernel
250. NeurIPS 2025Universal Causal Inference in a Topos

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.