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 1 of 5

1. ICML 20263DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification
2. ICML 20263DGS$^2$-TR: A Scalable Second-Order Trust-Region Method for 3D Gaussian Splatting
3. ICML 2026A Diffusive Classification Loss for Learning Energy-based Generative Models
4. ICML 2026A unified theory of feature learning in RNNs and DNNs
5. ICML 2026AI4SLT: Empirical Processes in Lean 4 for Formal Statistical Learning Theory
6. ICML 2026ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization
7. ICML 2026Allocating Variance to Maximize Expectation
8. ICML 2026Belief Propagation Converges to Gaussian Distributions in Sparsely-Connected Factor Graphs
9. ICML 2026Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes
10. ICML 2026Closing the Sim-to-Real Gap in Non-Markovian Spreading Processes via GPU-Accelerated Distributional RL
11. ICML 2026CoverPruneGS: Coverage-Preserving Structured Pruning for Hierarchical 3D Gaussian Splatting from Sparse-View Monocular Videos
12. ICML 2026De4D-SLAM: Gradient-Isolated Static-Dynamic Decoupling for Monocular SLAM in Dynamic Environments
13. ICML 2026Deep Learning of Compositional Targets with Hierarchical Spectral Methods
14. ICML 2026Deep Single-Index Fréchet Regression
15. ICML 2026Dense associative memory for Gaussian distributions
16. ICML 2026Dimension-free convergence of diffusion models for approximate Gaussian mixtures
17. ICML 2026Doubly Regularized Markov Decision Processes for Robust Reinforcement Learning
18. ICML 2026Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes
19. ICML 2026Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
20. ICML 2026EigenCache: Rethinking Diffusion Acceleration as Covariance-Optimal Forecasting and Submodular Information Allocation
21. ICML 2026Empirical Gaussian Processes
22. ICML 2026Even Faster Kernel Matrix Linear Algebra via Density Estimation
23. ICML 2026Fast Mixing Steady-State Control in Markov Decision Processes
24. ICML 2026Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
25. ICML 2026Flexible Kernels for Protein Property Prediction
26. ICML 2026Flow Sampling : Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
27. ICML 2026From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
28. ICML 2026Gaussian Mean Field Variational Inference can Overestimate Predictive Variance
29. ICML 2026GIPO: Gaussian Importance Sampling Policy Optimization
30. ICML 2026Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
31. ICML 2026gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
32. ICML 2026Hard labels sampled from sparse targets mislead rotation invariant algorithms
33. ICML 2026Hybrid Reinforcement Learning in Adversarial Markov Decision Processes
34. ICML 2026Identifying Common Hubs in Multiple Gaussian Graphical Models
35. ICML 2026Incremental Transformer Neural Processes
36. ICML 2026Interventional Processes For Causal Uncertainty Quantification
37. ICML 2026Is the Last Layer Sufficient for Uncertainty Quantification?
38. ICML 2026ITSPACE: Monotone Gaussian Optimal Transport Updates
39. ICML 2026Joint Learning in the Gaussian Single Index Model
40. ICML 2026Kinematics-Driven Gaussian Shape Deformation for Blurry Monocular Dynamic Scenes
41. ICML 2026Latent Space Robust Optimization of Neural Processes with Aligned Stratified Order-Statistic Loss Reduction
42. ICML 2026Learning 3D-Gaussian Simulators from RGB Videos
43. ICML 2026Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
44. ICML 2026Linear Regression with Unknown Truncation Beyond Gaussian Features
45. ICML 2026Local Constrained Bayesian Optimization
46. ICML 2026Local Policies for Graph-Structured Markov Decision Processes
47. ICML 2026Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
48. ICML 2026Monotonic Variational Gaussian Process for Efficient Data Collection
49. ICML 2026Networked Information Aggregation for Binary Classification
50. ICML 2026Nonparametric Distribution Regression Re-calibration

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.