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 231 篇，第 3 / 5 页

101. ICML 2024Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
102. ICML 2024Geometry-Aware Instrumental Variable Regression
103. ICML 2024Guidance with Spherical Gaussian Constraint for Conditional Diffusion
104. ICML 2024Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing
105. ICML 2024Interacting Diffusion Processes for Event Sequence Forecasting
106. ICML 2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
107. ICML 2024Learning in Deep Factor Graphs with Gaussian Belief Propagation
108. ICML 2024Learning Latent Space Hierarchical EBM Diffusion Models
109. ICML 2024Learning Mixtures of Gaussian Processes through Random Projection
110. ICML 2024Learning to Reach Goals via Diffusion
111. ICML 2024Logistic Variational Bayes Revisited
112. ICML 2024Memory Efficient Neural Processes via Constant Memory Attention Block
113. ICML 2024Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
114. ICML 2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
115. ICML 2024Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
116. ICML 2024Neural Jump-Diffusion Temporal Point Processes
117. ICML 2024Neuro-Symbolic Temporal Point Processes
118. ICML 2024No Double Descent in Principal Component Regression: A High-Dimensional Analysis
119. ICML 2024On the Consistency of Kernel Methods with Dependent Observations
120. ICML 2024One-Shot Strategic Classification Under Unknown Costs
121. ICML 2024PAGER: Accurate Failure Characterization in Deep Regression Models
122. ICML 2024Physics and Lie symmetry informed Gaussian processes
123. ICML 2024Preventing Model Collapse in Gaussian Process Latent Variable Models
124. ICML 2024Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
125. ICML 2024Probabilistic Generating Circuits - Demystified
126. ICML 2024Probabilistic Modeling of Interpersonal Coordination Processes
127. ICML 2024Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
128. ICML 2024Rate-Optimal Policy Optimization for Linear Markov Decision Processes
129. ICML 2024Robust and Conjugate Gaussian Process Regression
130. ICML 2024Robust Classification via a Single Diffusion Model
131. ICML 2024Robustly Learning Single-Index Models via Alignment Sharpness
132. ICML 2024Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
133. ICML 2024Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
134. ICML 2024Stationarity without mean reversion in improper Gaussian processes
135. ICML 2024Stochastic Localization via Iterative Posterior Sampling
136. ICML 2024Theoretical insights for diffusion guidance: A case study for Gaussian mixture models
137. ICML 2024Total Variation Floodgate for Variable Importance Inference in Classification
138. ICML 2024Translation Equivariant Transformer Neural Processes
139. ICML 2024Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression
140. ICML 2024What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
141. NeurIPS 2024A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers
142. NeurIPS 2024Achieving Constant Regret in Linear Markov Decision Processes
143. NeurIPS 2024Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures
144. NeurIPS 2024Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
145. NeurIPS 2024Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes
146. NeurIPS 2024Approximately Equivariant Neural Processes
147. NeurIPS 2024Approximation-Aware Bayesian Optimization
148. NeurIPS 2024Batched Energy-Entropy acquisition for Bayesian Optimization
149. NeurIPS 2024Bayesian Adaptive Calibration and Optimal Design
150. NeurIPS 2024Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing

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。