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

69 papers, page 1 of 2

1. NeurIPS 2023A Bayesian Take on Gaussian Process Networks
2. NeurIPS 2023Adaptive Linear Estimating Equations
3. NeurIPS 2023Add and Thin: Diffusion for Temporal Point Processes
4. NeurIPS 2023Automatic Integration for Spatiotemporal Neural Point Processes
5. NeurIPS 2023Batch Bayesian Optimization For Replicable Experimental Design
6. NeurIPS 2023Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability
7. NeurIPS 2023Bayesian Optimization with Cost-varying Variable Subsets
8. NeurIPS 2023Beta Diffusion
9. NeurIPS 2023Beyond Average Return in Markov Decision Processes
10. NeurIPS 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
11. NeurIPS 2023Bias in Evaluation Processes: An Optimization-Based Model
12. NeurIPS 2023Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
13. NeurIPS 2023CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
14. NeurIPS 2023Collaboratively Learning Linear Models with Structured Missing Data
15. NeurIPS 2023Compositional Sculpting of Iterative Generative Processes
16. NeurIPS 2023Demystifying Softmax Gating Function in Gaussian Mixture of Experts
17. NeurIPS 2023Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
18. NeurIPS 2023Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
19. NeurIPS 2023End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
20. NeurIPS 2023Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
21. NeurIPS 2023Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
22. NeurIPS 2023Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
23. NeurIPS 2023Failure-Aware Gaussian Process Optimization with Regret Bounds
24. NeurIPS 2023GAUCHE: A Library for Gaussian Processes in Chemistry
25. NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
26. NeurIPS 2023Gaussian Membership Inference Privacy
27. NeurIPS 2023Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
28. NeurIPS 2023Geometric Neural Diffusion Processes
29. NeurIPS 2023GPEX, A Framework For Interpreting Artificial Neural Networks
30. NeurIPS 2023Graph-Structured Gaussian Processes for Transferable Graph Learning
31. NeurIPS 2023Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
32. NeurIPS 2023Implicit Manifold Gaussian Process Regression
33. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
34. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
35. NeurIPS 2023Kernelized Reinforcement Learning with Order Optimal Regret Bounds
36. NeurIPS 2023Learning Causal Models under Independent Changes
37. NeurIPS 2023Learning Curves for Deep Structured Gaussian Feature Models
38. NeurIPS 2023Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
39. NeurIPS 2023Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
40. NeurIPS 2023Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
41. NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
42. NeurIPS 2023On Differentially Private Sampling from Gaussian and Product Distributions
43. NeurIPS 2023On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
44. NeurIPS 2023On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
45. NeurIPS 2023Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
46. NeurIPS 2023Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
47. NeurIPS 2023Policy Gradient for Rectangular Robust Markov Decision Processes
48. NeurIPS 2023Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
49. NeurIPS 2023Practical Equivariances via Relational Conditional Neural Processes
50. NeurIPS 2023Precise asymptotic generalization for multiclass classification with overparameterized linear models

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.