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 53 篇，第 1 / 2 页

1. ICML 2025Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
2. ICML 2025BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
3. ICML 2025Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models
4. ICML 2025Determinant Estimation under Memory Constraints and Neural Scaling Laws
5. ICML 2025Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors
6. ICML 2025Distributionally Robust Active Learning for Gaussian Process Regression
7. ICML 2025Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements
8. ICML 2025Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
9. ICML 2025Finite-Time Analysis of Discrete-Time Stochastic Interpolants
10. ICML 2025Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
11. ICML 2025Global Optimization with a Power-Transformed Objective and Gaussian Smoothing
12. ICML 2025Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables
13. ICML 2025Gradient Flow Provably Learns Robust Classifiers for Orthonormal GMMs
14. ICML 2025Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation
15. ICML 2025Gridded Transformer Neural Processes for Spatio-Temporal Data
16. ICML 2025Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
17. ICML 2025Integration-free Kernels for Equivariant Gaussian Process Modelling
18. ICML 2025Interaction-Aware Gaussian Weighting for Clustered Federated Learning
19. ICML 2025K$^2$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes
20. ICML 2025Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
21. ICML 2025Learning Gaussian DAG Models without Condition Number Bounds
22. ICML 2025Learning multivariate Gaussians with imperfect advice
23. ICML 2025Learning Robust Neural Processes with Risk-Averse Stochastic Optimization
24. ICML 2025Learning Safe Control via On-the-Fly Bandit Exploration
25. ICML 2025Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes
26. ICML 2025Learning Utilities from Demonstrations in Markov Decision Processes
27. ICML 2025Linearization Turns Neural Operators into Function-Valued Gaussian Processes
28. ICML 2025Low-Rank Thinning
29. ICML 2025Model-Based Exploration in Monitored Markov Decision Processes
30. ICML 2025Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling
31. ICML 2025New Bounds for Sparse Variational Gaussian Processes
32. ICML 2025On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
33. ICML 2025PAC-Bayes Analysis for Recalibration in Classification
34. ICML 2025Pareto-frontier Entropy Search with Variational Lower Bound Maximization
35. ICML 2025PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting for Novel View Synthesis
36. ICML 2025PRIME: Deep Imbalanced Regression with Proxies
37. ICML 2025Quantum Algorithms for Finite-horizon Markov Decision Processes
38. ICML 2025Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
39. ICML 2025Rényi Neural Processes
40. ICML 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
41. ICML 2025Robust and Conjugate Spatio-Temporal Gaussian Processes
42. ICML 2025Sample Complexity of Correlation Detection in the Gaussian Wigner Model
43. ICML 2025Scalable Gaussian Processes with Latent Kronecker Structure
44. ICML 2025Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
45. ICML 2025Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization
46. ICML 2025Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise
47. ICML 2025Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
48. ICML 2025Stream-level Flow Matching with Gaussian Processes
49. ICML 2025The Price of Linear Time: Error Analysis of Structured Kernel Interpolation
50. ICML 2025Toward Efficient Kernel-Based Solvers for Nonlinear PDEs

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。