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

637 papers, page 6 of 13

251. NeurIPS 2025HoT-VI: Reparameterizable Variational Inference for Capturing Instance-Level High-Order Correlations
252. NeurIPS 2025Identifying Macro Causal Effects in C-DMGs over DMGs
253. NeurIPS 2025Image Super-Resolution with Guarantees via Conformalized Generative Models
254. NeurIPS 2025Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
255. NeurIPS 2025Infinite Neural Operators: Gaussian processes on functions
256. NeurIPS 2025Informed Initialization for Bayesian Optimization and Active Learning
257. NeurIPS 2025Integral Imprecise Probability Metrics
258. NeurIPS 2025Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
259. NeurIPS 2025Large Language Bayes
260. NeurIPS 2025Learning Counterfactual Outcomes Under Rank Preservation
261. NeurIPS 2025Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
262. NeurIPS 2025Learning normalized image densities via dual score matching
263. NeurIPS 2025Learning to Condition: A Neural Heuristic for Scalable MPE Inference
264. NeurIPS 2025Least squares variational inference
265. NeurIPS 2025Less Greedy Equivalence Search
266. NeurIPS 2025Leveraging semantic similarity for experimentation with AI-generated treatments
267. NeurIPS 2025List-Level Distribution Coupling with Applications to Speculative Decoding and Lossy Compression
268. NeurIPS 2025Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
269. NeurIPS 2025Low-Rank Graphon Learning for Networks
270. NeurIPS 2025Martingale Posterior Neural Networks for Fast Sequential Decision Making
271. NeurIPS 2025Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise
272. NeurIPS 2025MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
273. NeurIPS 2025Measure-Theoretic Anti-Causal Representation Learning
274. NeurIPS 2025Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo
275. NeurIPS 2025MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions
276. NeurIPS 2025Model Reconciliation via Cost-Optimal Explanations in Probabilistic Logic Programming
277. NeurIPS 2025Model-Informed Flows for Bayesian Inference
278. NeurIPS 2025Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability Graphs
279. NeurIPS 2025Multi-View Oriented GPLVM: Expressiveness and Efficiency
280. NeurIPS 2025Multilevel neural simulation-based inference
281. NeurIPS 2025Multivariate Latent Recalibration for Conditional Normalizing Flows
282. NeurIPS 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
283. NeurIPS 2025Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference
284. NeurIPS 2025Neural Hamiltonian Diffusions for Modeling Structured Geometric Dynamics
285. NeurIPS 2025Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
286. NeurIPS 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
287. NeurIPS 2025Neurosymbolic Diffusion Models
288. NeurIPS 2025NeuSymEA: Neuro-symbolic Entity Alignment via Variational Inference
289. NeurIPS 2025Non-equilibrium Annealed Adjoint Sampler
290. NeurIPS 2025Non-exchangeable Conformal Prediction with Optimal Transport: Tackling Distribution Shift with Unlabeled Data
291. NeurIPS 2025Non-Stationary Structural Causal Bandits
292. NeurIPS 2025Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
293. NeurIPS 2025On scalable and efficient training of diffusion samplers
294. NeurIPS 2025On the Hardness of Approximating Distributions with Tractable Probabilistic Models
295. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
296. NeurIPS 2025One Sample is Enough to Make Conformal Prediction Robust
297. NeurIPS 2025Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
298. NeurIPS 2025Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect
299. NeurIPS 2025Optimal Nuisance Function Tuning for Estimating a Doubly Robust Functional under Proportional Asymptotics
300. NeurIPS 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data

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.