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 154 篇，第 2 / 4 页

51. ICML 2023Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
52. ICML 2023Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
53. ICML 2023High Probability Convergence of Stochastic Gradient Methods
54. ICML 2023Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes
55. ICML 2023Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds
56. ICML 2023Infinite Action Contextual Bandits with Reusable Data Exhaust
57. ICML 2023Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network
58. ICML 2023Markovian Gaussian Process Variational Autoencoders
59. ICML 2023Memory-Based Dual Gaussian Processes for Sequential Learning
60. ICML 2023Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
61. ICML 2023Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals
62. ICML 2023Neural Diffusion Processes
63. ICML 2023Neural Markov Jump Processes
64. ICML 2023NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
65. ICML 2023On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances
66. ICML 2023On the Estimation of Gaussian Mixture Copula Models
67. ICML 2023On the Optimality of Misspecified Kernel Ridge Regression
68. ICML 2023On the Training Instability of Shuffling SGD with Batch Normalization
69. ICML 2023Optimizing Hyperparameters with Conformal Quantile Regression
70. ICML 2023Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
71. ICML 2023Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
72. ICML 2023Random Grid Neural Processes for Parametric Partial Differential Equations
73. ICML 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
74. ICML 2023Regression with Label Permutation in Generalized Linear Model
75. ICML 2023Regression with Sensor Data Containing Incomplete Observations
76. ICML 2023Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents
77. ICML 2023Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
78. ICML 2023Robust Satisficing MDPs
79. ICML 2023Simplex Random Features
80. ICML 2023Sketched Ridgeless Linear Regression: The Role of Downsampling
81. ICML 2023Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
82. ICML 2023Strategic Classification with Unknown User Manipulations
83. ICML 2023Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis
84. ICML 2023Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
85. ICML 2023Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization
86. NeurIPS 2023A Bayesian Take on Gaussian Process Networks
87. NeurIPS 2023Adaptive Linear Estimating Equations
88. NeurIPS 2023Add and Thin: Diffusion for Temporal Point Processes
89. NeurIPS 2023Automatic Integration for Spatiotemporal Neural Point Processes
90. NeurIPS 2023Batch Bayesian Optimization For Replicable Experimental Design
91. NeurIPS 2023Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability
92. NeurIPS 2023Bayesian Optimization with Cost-varying Variable Subsets
93. NeurIPS 2023Beta Diffusion
94. NeurIPS 2023Beyond Average Return in Markov Decision Processes
95. NeurIPS 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
96. NeurIPS 2023Bias in Evaluation Processes: An Optimization-Based Model
97. NeurIPS 2023Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
98. NeurIPS 2023CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
99. NeurIPS 2023Collaboratively Learning Linear Models with Structured Missing Data
100. NeurIPS 2023Compositional Sculpting of Iterative Generative Processes

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。