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 6 of 9

251. NeurIPS 2024Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
252. NeurIPS 2024Adjust Pearson's $r$ to Measure Arbitrary Monotone Dependence
253. NeurIPS 2024Amortized Bayesian Experimental Design for Decision-Making
254. NeurIPS 2024Amortizing intractable inference in diffusion models for vision, language, and control
255. NeurIPS 2024An Analysis of Elo Rating Systems via Markov Chains
256. NeurIPS 2024An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations
257. NeurIPS 2024An Information Theoretic Perspective on Conformal Prediction
258. NeurIPS 2024Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing
259. NeurIPS 2024Approximately Equivariant Neural Processes
260. NeurIPS 2024Approximation-Aware Bayesian Optimization
261. NeurIPS 2024Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families
262. NeurIPS 2024Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments
263. NeurIPS 2024Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing
264. NeurIPS 2024Bayesian Online Natural Gradient (BONG)
265. NeurIPS 2024Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
266. NeurIPS 2024BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
267. NeurIPS 2024Block Sparse Bayesian Learning: A Diversified Scheme
268. NeurIPS 2024BMRS: Bayesian Model Reduction for Structured Pruning
269. NeurIPS 2024Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models
270. NeurIPS 2024Community Detection Guarantees using Embeddings Learned by Node2Vec
271. NeurIPS 2024Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
272. NeurIPS 2024Conditional Density Estimation with Histogram Trees
273. NeurIPS 2024Conformalized Credal Set Predictors
274. NeurIPS 2024Conjugate Bayesian Two-step Change Point Detection for Hawkes Process
275. NeurIPS 2024Consistency Models for Scalable and Fast Simulation-Based Inference
276. NeurIPS 2024Constrained Binary Decision Making
277. NeurIPS 2024Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
278. NeurIPS 2024Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
279. NeurIPS 2024Credal Deep Ensembles for Uncertainty Quantification
280. NeurIPS 2024Decomposable Transformer Point Processes
281. NeurIPS 2024Deep Learning for Computing Convergence Rates of Markov Chains
282. NeurIPS 2024Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors
283. NeurIPS 2024Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
284. NeurIPS 2024EigenVI: score-based variational inference with orthogonal function expansions
285. NeurIPS 2024Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA
286. NeurIPS 2024Expected Probabilistic Hierarchies
287. NeurIPS 2024Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
288. NeurIPS 2024eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling
289. NeurIPS 2024FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings
290. NeurIPS 2024Fast Channel Simulation via Error-Correcting Codes
291. NeurIPS 2024Fearless Stochasticity in Expectation Propagation
292. NeurIPS 2024Foundation Inference Models for Markov Jump Processes
293. NeurIPS 2024FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
294. NeurIPS 2024Functional Gradient Flows for Constrained Sampling
295. NeurIPS 2024Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
296. NeurIPS 2024Gaussian Process Bandits for Top-k Recommendations
297. NeurIPS 2024General bounds on the quality of Bayesian coresets
298. NeurIPS 2024Generative Adversarial Model-Based Optimization via Source Critic Regularization
299. NeurIPS 2024Globally Convergent Variational Inference
300. NeurIPS 2024Gradient-based Discrete Sampling with Automatic Cyclical Scheduling

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.