高斯过程论文趋势 2023–2026

主要结论

• 高斯过程论文从 2023 年的 154 篇增长到 2026 年的 341 篇，三年间扩大到约 2.2 倍。
• NeurIPS 是发高斯过程论文最多的会议，2023–2026 年共 372 篇，占四个会议总量的 35%。
• 交叉方向里涨得最快的是三维视觉：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 158 篇。
• 在「概率与贝叶斯」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的生成流是 1.9 倍，高斯过程是 2.2 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部高斯过程论文

共 244 篇，第 1 / 5 页

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

常见问题

2023–2026 年四大会议一共有多少篇高斯过程论文？

共 1,049 篇，按年份分别是 2023 年 154 篇、2024 年 231 篇、2025 年 323 篇、2026 年 341 篇。

NeurIPS 2026 有多少篇高斯过程论文？

NeurIPS 2026 有 106 篇高斯过程论文，上一届是 106 篇。

ICLR 2026 有多少篇高斯过程论文？

ICLR 2026 有 40 篇高斯过程论文，上一届是 41 篇。

ICML 2026 有多少篇高斯过程论文？

ICML 2026 有 79 篇高斯过程论文，上一届是 53 篇。

CVPR 2026 有多少篇高斯过程论文？

CVPR 2026 有 116 篇高斯过程论文，上一届是 123 篇。

哪个会议的高斯过程论文最多？

NeurIPS，2023–2026 年共 372 篇。