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 4 of 13

151. ICML 2025The Limits of Tractable Marginalization
152. ICML 2025To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
153. ICML 2025TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation
154. ICML 2025Trajectory Inference with Smooth Schrödinger Bridges
155. ICML 2025Variational Learning of Fractional Posteriors
156. ICML 2025Variational Phylogenetic Inference with Products over Bipartitions
157. ICML 2025Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
158. ICML 2025Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
159. NeurIPS 2025$\text{G}^2\text{M}$: A Generalized Gaussian Mirror Method to Boost Feature Selection Power
160. NeurIPS 2025A Black-Box Debiasing Framework for Conditional Sampling
161. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
162. NeurIPS 2025A Counterfactual Semantics for Hybrid Dynamical Systems
163. NeurIPS 2025A Hierarchy of Graphical Models for Counterfactual Inferences
164. NeurIPS 2025A Latent Multilayer Graphical Model For Complex, Interdependent Systems
165. NeurIPS 2025A Unified Framework for the Transportability of Population-Level Causal Measures
166. NeurIPS 2025Abstract Counterfactuals for Language Model Agents
167. NeurIPS 2025Accelerating Feature Conformal Prediction via Taylor Approximation
168. NeurIPS 2025Adaptive Data-Borrowing for Improving Treatment Effect Estimation using External Controls
169. NeurIPS 2025Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
170. NeurIPS 2025Adjoint Schrödinger Bridge Sampler
171. NeurIPS 2025Agents Robust to Distribution Shifts Learn Causal World Models Even Under Mediation
172. NeurIPS 2025ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
173. NeurIPS 2025Amortized Active Generation of Pareto Sets
174. NeurIPS 2025Amortized Variational Transdimensional Inference
175. NeurIPS 2025An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
176. NeurIPS 2025Asymptotically exact variational flows via involutive MCMC kernels
177. NeurIPS 2025Bayesian Concept Bottleneck Models with LLM Priors
178. NeurIPS 2025BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
179. NeurIPS 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliance
180. NeurIPS 2025Bi-Directional Communication-Efficient Stochastic FL via Remote Source Generation
181. NeurIPS 2025Brain-like Variational Inference
182. NeurIPS 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
183. NeurIPS 2025Causal Discovery over Clusters of Variables in Markovian Systems
184. NeurIPS 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
185. NeurIPS 2025Certifying Deep Network Risks and Individual Predictions with PAC-Bayes Loss via Localized Priors
186. NeurIPS 2025Channel Simulation and Distributed Compression with Ensemble Rejection Sampling
187. NeurIPS 2025Characterization and Learning of Causal Graphs from Hard Interventions
188. NeurIPS 2025Collaborative and Confidential Junction Trees for Hybrid Bayesian Networks
189. NeurIPS 2025CoLT: The conditional localization test for assessing the accuracy of neural posterior estimates
190. NeurIPS 2025Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
191. NeurIPS 2025Conformal Prediction for Causal Effects of Continuous Treatments
192. NeurIPS 2025Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
193. NeurIPS 2025Conformal Prediction for Time-series Forecasting with Change Points
194. NeurIPS 2025Conformal Prediction under Lévy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations
195. NeurIPS 2025Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
196. NeurIPS 2025Confounding Robust Deep Reinforcement Learning: A Causal Approach
197. NeurIPS 2025Connecting Jensen–Shannon and Kullback–Leibler Divergences: A New Bound for Representation Learning
198. NeurIPS 2025Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective
199. NeurIPS 2025Counterfactual Image Editing with Disentangled Causal Latent Space
200. NeurIPS 2025Counterfactual Implicit Feedback Modeling

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.