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 8 of 9

351. NeurIPS 2024Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information
352. NeurIPS 2024Small coresets via negative dependence: DPPs, linear statistics, and concentration
353. NeurIPS 2024Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation
354. NeurIPS 2024Sparse Bayesian Generative Modeling for Compressive Sensing
355. NeurIPS 2024Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
356. NeurIPS 2024Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
357. NeurIPS 2024Stopping Bayesian Optimization with Probabilistic Regret Bounds
358. NeurIPS 2024Streaming Bayes GFlowNets
359. NeurIPS 2024Symmetries in Overparametrized Neural Networks: A Mean Field View
360. NeurIPS 2024This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
361. NeurIPS 2024To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty
362. NeurIPS 2024Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy
363. NeurIPS 2024Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees
364. NeurIPS 2024TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing
365. NeurIPS 2024UDPM: Upsampling Diffusion Probabilistic Models
366. NeurIPS 2024Unscrambling disease progression at scale: fast inference of event permutations with optimal transport
367. NeurIPS 2024VISA: Variational Inference with Sequential Sample-Average Approximations
368. NeurIPS 2024Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
369. NeurIPS 2024What type of inference is planning?
370. NeurIPS 2024Why the Metric Backbone Preserves Community Structure
371. NeurIPS 2024Your contrastive learning problem is secretly a distribution alignment problem
372. NeurIPS 2023$L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting
373. NeurIPS 2023A Computationally Efficient Sparsified Online Newton Method
374. NeurIPS 2023A Heavy-Tailed Algebra for Probabilistic Programming
375. NeurIPS 2023A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
376. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
377. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
378. NeurIPS 2023Block Broyden's Methods for Solving Nonlinear Equations
379. NeurIPS 2023Byzantine-Tolerant Methods for Distributed Variational Inequalities
380. NeurIPS 2023Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
381. NeurIPS 2023Characteristic Circuits
382. NeurIPS 2023CORL: Research-oriented Deep Offline Reinforcement Learning Library
383. NeurIPS 2023Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
384. NeurIPS 2023Credal Marginal MAP
385. NeurIPS 2023Deep Fractional Fourier Transform
386. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
387. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
388. NeurIPS 2023Deep Recurrent Optimal Stopping
389. NeurIPS 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
390. NeurIPS 2023Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation
391. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
392. NeurIPS 2023Faster Differentially Private Convex Optimization via Second-Order Methods
393. NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
394. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
395. NeurIPS 2023How a Student becomes a Teacher: learning and forgetting through Spectral methods
396. NeurIPS 2023How many samples are needed to leverage smoothness?
397. NeurIPS 2023Human spatiotemporal pattern learning as probabilistic program synthesis
398. NeurIPS 2023Imitation Learning from Vague Feedback
399. NeurIPS 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
400. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics

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.