Gaussian Processes papers, 2023–2026

Key findings

• Gaussian Processes went from 154 papers in 2023 to 341 in 2026, roughly 2.2x in three years.
• NeurIPS publishes the most Gaussian Processes work: 372 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with 3D Vision: papers tagged with both rose from 0 in 2023 to 158 in 2026.
• Among the 5 topics under Probabilistic and Bayesian, it ranks #1 by 2026 paper count; the fastest grower in the group, Generative Flows, is up 1.9x versus 2.2x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
• ICML 2026A unified theory of feature learning in RNNs and DNNs
• ICLR 2026Adaptive Gaussian Expansion for On-the-fly Category Discovery
• CVPR 2026Distilling Unsigned Distance Function for Surface Reconstruction from 3D Gaussian
• NeurIPS 2025Accelerated Evolving Set Processes for Local PageRank Computation
• ICML 2025BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
• ICLR 2025Boosting Methods for Interval-censored Data with Regression and Classification
• CVPR 20253D Gaussian Head Avatars with Expressive Dynamic Appearances by Compact Tensorial Representations
• NeurIPS 2024Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
• ICML 2024A Rate-Distortion View of Uncertainty Quantification
• ICLR 2024A General Framework for User-Guided Bayesian Optimization
• CVPR 2024Deep Imbalanced Regression via Hierarchical Classification Adjustment
• NeurIPS 2023A Bayesian Take on Gaussian Process Networks
• ICML 2023Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation
• ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
• CVPR 2023Robust and Scalable Gaussian Process Regression and Its Applications
• NeurIPS 2026Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
• ICML 2026ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization
• ICLR 2026Continuous multinomial logistic regression for neural decoding
• CVPR 2026ELITE Efficient Gaussian Head Avatar from a Monocular Video via

All Gaussian Processes papers

244 papers, page 4 of 5

151. ICML 2024Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic
152. ICML 2024EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
153. ICML 2024First-Order Manifold Data Augmentation for Regression Learning
154. ICML 2024Gaussian Plane-Wave Neural Operator for Electron Density Estimation
155. ICML 2024Gaussian Processes on Cellular Complexes
156. ICML 2024GaussianPro: 3D Gaussian Splatting with Progressive Propagation
157. ICML 2024Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
158. ICML 2024Geometry-Aware Instrumental Variable Regression
159. ICML 2024Guidance with Spherical Gaussian Constraint for Conditional Diffusion
160. ICML 2024Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing
161. ICML 2024Interacting Diffusion Processes for Event Sequence Forecasting
162. ICML 2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
163. ICML 2024Learning in Deep Factor Graphs with Gaussian Belief Propagation
164. ICML 2024Learning Latent Space Hierarchical EBM Diffusion Models
165. ICML 2024Learning Mixtures of Gaussian Processes through Random Projection
166. ICML 2024Learning to Reach Goals via Diffusion
167. ICML 2024Logistic Variational Bayes Revisited
168. ICML 2024Memory Efficient Neural Processes via Constant Memory Attention Block
169. ICML 2024Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
170. ICML 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
171. ICML 2024Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
172. ICML 2024Neural Jump-Diffusion Temporal Point Processes
173. ICML 2024Neuro-Symbolic Temporal Point Processes
174. ICML 2024No Double Descent in Principal Component Regression: A High-Dimensional Analysis
175. ICML 2024On the Consistency of Kernel Methods with Dependent Observations
176. ICML 2024One-Shot Strategic Classification Under Unknown Costs
177. ICML 2024PAGER: Accurate Failure Characterization in Deep Regression Models
178. ICML 2024Physics and Lie symmetry informed Gaussian processes
179. ICML 2024Preventing Model Collapse in Gaussian Process Latent Variable Models
180. ICML 2024Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
181. ICML 2024Probabilistic Generating Circuits - Demystified
182. ICML 2024Probabilistic Modeling of Interpersonal Coordination Processes
183. ICML 2024Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
184. ICML 2024Rate-Optimal Policy Optimization for Linear Markov Decision Processes
185. ICML 2024Robust and Conjugate Gaussian Process Regression
186. ICML 2024Robust Classification via a Single Diffusion Model
187. ICML 2024Robustly Learning Single-Index Models via Alignment Sharpness
188. ICML 2024Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
189. ICML 2024Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
190. ICML 2024Stationarity without mean reversion in improper Gaussian processes
191. ICML 2024Stochastic Localization via Iterative Posterior Sampling
192. ICML 2024Theoretical insights for diffusion guidance: A case study for Gaussian mixture models
193. ICML 2024Total Variation Floodgate for Variable Importance Inference in Classification
194. ICML 2024Translation Equivariant Transformer Neural Processes
195. ICML 2024Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
196. ICML 2024What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
197. ICML 2023A Closer Look at Few-shot Classification Again
198. ICML 2023Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation
199. ICML 2023Cluster-Specific Predictions with Multi-Task Gaussian Processes
200. ICML 2023Communication-Constrained Bandits under Additive Gaussian Noise

FAQ

How many Gaussian Processes papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,049 in total: 154 in 2023, 231 in 2024, 323 in 2025, 341 in 2026.

How many Gaussian Processes papers are at NeurIPS 2026?

NeurIPS 2026 has 106 Gaussian Processes papers, versus 106 the year before.

How many Gaussian Processes papers are at ICLR 2026?

ICLR 2026 has 40 Gaussian Processes papers, versus 41 the year before.

How many Gaussian Processes papers are at ICML 2026?

ICML 2026 has 79 Gaussian Processes papers, versus 53 the year before.

How many Gaussian Processes papers are at CVPR 2026?

CVPR 2026 has 116 Gaussian Processes papers, versus 123 the year before.

Which conference publishes the most Gaussian Processes papers?

NeurIPS, with 372 papers from 2023 to 2026.