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 27 篇，第 1 / 1 页

1. ICLR 2023A Kernel Perspective of Skip Connections in Convolutional Networks
2. ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
3. ICLR 2023Autoregressive Conditional Neural Processes
4. ICLR 2023Bridge the Inference Gaps of Neural Processes via Expectation Maximization
5. ICLR 2023Calibrating Transformers via Sparse Gaussian Processes
6. ICLR 2023Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?
7. ICLR 2023Deep Variational Implicit Processes
8. ICLR 2023Gradient Boosting Performs Gaussian Process Inference
9. ICLR 2023Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
10. ICLR 2023Gray-Box Gaussian Processes for Automated Reinforcement Learning
11. ICLR 2023Improving Deep Regression with Ordinal Entropy
12. ICLR 2023Individual Privacy Accounting with Gaussian Differential Privacy
13. ICLR 2023Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
14. ICLR 2023Latent Bottlenecked Attentive Neural Processes
15. ICLR 2023Learning Adversarial Linear Mixture Markov Decision Processes with Bandit Feedback and Unknown Transition
16. ICLR 2023Learning Label Encodings for Deep Regression
17. ICLR 2023Martingale Posterior Neural Processes
18. ICLR 2023Meta Temporal Point Processes
19. ICLR 2023Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
20. ICLR 2023Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics
21. ICLR 2023Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
22. ICLR 2023Semi-Parametric Inducing Point Networks and Neural Processes
23. ICLR 2023SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning
24. ICLR 2023Spatio-temporal point processes with deep non-stationary kernels
25. ICLR 2023Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning
26. ICLR 2023Versatile Neural Processes for Learning Implicit Neural Representations
27. ICLR 2023VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis

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。