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 12 of 13

551. ICML 2023Direct Parameterization of Lipschitz-Bounded Deep Networks
552. ICML 2023FARE: Provably Fair Representation Learning with Practical Certificates
553. ICML 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspective
554. ICML 2023Learning Instance-Specific Augmentations by Capturing Local Invariances
555. ICML 2023Nearly-tight Bounds for Deep Kernel Learning
556. ICML 2023Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits
557. ICML 2023OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
558. ICML 2023On the Convergence Rates of Policy Gradient Methods
559. ICML 2023Principled Acceleration of Iterative Numerical Methods Using Machine Learning
560. ICML 2023Probabilistic Attention-to-Influence Neural Models for Event Sequences
561. ICML 2023Probabilistic Categorical Adversarial Attack and Adversarial Training
562. ICML 2023Probabilistic Concept Bottleneck Models
563. ICML 2023Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
564. ICML 2023Probabilistic Imputation for Time-series Classification with Missing Data
565. ICML 2023Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
566. ICML 2023Quantile Credit Assignment
567. ICML 2023Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
568. ICML 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
569. NeurIPS 2023$L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting
570. NeurIPS 2023A Computationally Efficient Sparsified Online Newton Method
571. NeurIPS 2023A Heavy-Tailed Algebra for Probabilistic Programming
572. NeurIPS 2023A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
573. NeurIPS 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
574. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
575. NeurIPS 2023Block Broyden's Methods for Solving Nonlinear Equations
576. NeurIPS 2023Byzantine-Tolerant Methods for Distributed Variational Inequalities
577. NeurIPS 2023Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
578. NeurIPS 2023Characteristic Circuits
579. NeurIPS 2023CORL: Research-oriented Deep Offline Reinforcement Learning Library
580. NeurIPS 2023Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
581. NeurIPS 2023Credal Marginal MAP
582. NeurIPS 2023Deep Fractional Fourier Transform
583. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
584. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
585. NeurIPS 2023Deep Recurrent Optimal Stopping
586. NeurIPS 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
587. NeurIPS 2023Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation
588. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
589. NeurIPS 2023Faster Differentially Private Convex Optimization via Second-Order Methods
590. NeurIPS 2023FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
591. NeurIPS 2023Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
592. NeurIPS 2023How a Student becomes a Teacher: learning and forgetting through Spectral methods
593. NeurIPS 2023How many samples are needed to leverage smoothness?
594. NeurIPS 2023Human spatiotemporal pattern learning as probabilistic program synthesis
595. NeurIPS 2023Imitation Learning from Vague Feedback
596. NeurIPS 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
597. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
598. NeurIPS 2023Jigsaw: Learning to Assemble Multiple Fractured Objects
599. NeurIPS 2023Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
600. NeurIPS 2023Learning Functional Transduction

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.