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

231 papers, page 5 of 5

201. NeurIPS 2024Non-parametric classification via expand-and-sparsify representation
202. NeurIPS 2024Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
203. NeurIPS 2024ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings
204. NeurIPS 2024On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
205. NeurIPS 2024Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
206. NeurIPS 2024Physics-Informed Variational State-Space Gaussian Processes
207. NeurIPS 2024Piecewise deterministic generative models
208. NeurIPS 2024Pre-trained Gaussian Processes for Bayesian Optimization
209. NeurIPS 2024R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction
210. NeurIPS 2024Random Function Descent
211. NeurIPS 2024ReGS: Reference-based Controllable Scene Stylization with Gaussian Splatting
212. NeurIPS 2024Reliable Learning of Halfspaces under Gaussian Marginals
213. NeurIPS 2024Robust Gaussian Processes via Relevance Pursuit
214. NeurIPS 2024Robust Sparse Regression with Non-Isotropic Designs
215. NeurIPS 2024Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
216. NeurIPS 2024Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
217. NeurIPS 2024Sample-efficient Bayesian Optimisation Using Known Invariances
218. NeurIPS 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
219. NeurIPS 2024Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data
220. NeurIPS 2024Slight Corruption in Pre-training Data Makes Better Diffusion Models
221. NeurIPS 2024Small coresets via negative dependence: DPPs, linear statistics, and concentration
222. NeurIPS 2024Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting
223. NeurIPS 2024Spectral Learning of Shared Dynamics Between Generalized-Linear Processes
224. NeurIPS 2024Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
225. NeurIPS 2024Stopping Bayesian Optimization with Probabilistic Regret Bounds
226. NeurIPS 2024The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems
227. NeurIPS 2024Truncated Variance Reduced Value Iteration
228. NeurIPS 2024Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure
229. NeurIPS 2024Universality in Transfer Learning for Linear Models
230. NeurIPS 2024VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
231. NeurIPS 2024WildGaussians: 3D Gaussian Splatting In the Wild

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.