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 372 篇，第 2 / 8 页

51. NeurIPS 2026Learning Hierarchical Forward Processes For Discrete Diffusion Language Models
52. NeurIPS 2026LLM Flow Processes for Text-Conditioned Regression
53. NeurIPS 2026Local Gaussian Processes on Compact Lie Groups
54. NeurIPS 2026Local linear convergence of gradient methods for overparameterized Gaussian mixtures
55. NeurIPS 2026Mamba Flow Matching Neural Processes: Linear-Time Inference for Irregularly Observed Spatial Fields
56. NeurIPS 2026MCSplat: Multi-View Photometric and Geometric Consistent Feed-Forward Gaussian Splatting for Driving Scenes
57. NeurIPS 2026Mesh Invaiant Infinite Dimensional Adaptive MCMC for Latent Gaussian Processes
58. NeurIPS 2026Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification
59. NeurIPS 2026MOCHA: Discovering Multi-Order Dynamic Causal Structure in Temporal Point Processes
60. NeurIPS 2026Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP
61. NeurIPS 2026Multiclass Classification with Rare Useful Features: Fundamental Limits and the Optimality of Diversity Pursuit Higher Criticism
62. NeurIPS 2026MVPISplat: Multi-View Photometric Inconsistency for Defending 3D Gaussian Splatting Attacks
63. NeurIPS 2026Neural Backward Filtering Forward Guiding
64. NeurIPS 2026NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting
65. NeurIPS 2026One Avatar, Any Budget: Robust LoD for Dynamic Gaussian Avatars
66. NeurIPS 2026One-Bit Clustering for Two Component Sub-Gaussian Mixture Models
67. NeurIPS 2026Online Data Selection for Instruction Tuning via Gaussian Processes
68. NeurIPS 2026Optimal Linear Regression Without a Variance
69. NeurIPS 2026Outlier-Robust Multi-Output Gaussian Processes
70. NeurIPS 2026Periodic Complex Stochastic Processes for Retrieving Atomic Structures of Unknown Matters
71. NeurIPS 2026PF-SGS: Pose-Free Streaming 3D Gaussian Splatting for Large-Scale Scene Reconstruction
72. NeurIPS 2026PGMS: Pyramidal Gaussian Mixture Splatting for 3DGS Compression
73. NeurIPS 2026Phase Transitions in Heavy-Tailed Mean Estimation under $\ell_p$ Norms
74. NeurIPS 2026Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals
75. NeurIPS 2026PolySplat: Workload-Regime-Aware Rasterization for 3D Gaussian Splatting
76. NeurIPS 2026Post-ADC Inference: Valid Inference After Active Data Collection
77. NeurIPS 2026Practical Non-Stationary Graph Gaussian Processes
78. NeurIPS 2026Proper Agnostic Learning of Functions of Halfspaces
79. NeurIPS 2026Provable Pruning for Efficient 3D Gaussian Splatting via Coresets
80. NeurIPS 2026Rare-Tail Statistics For Learning Biased Gaussian Halfspaces with Label Noise
81. NeurIPS 2026Regret-Optimal Wasserstein-Robust Regression
82. NeurIPS 2026ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting
83. NeurIPS 2026Residual-Autoregressive Context for 3D Gaussian Splatting Compression
84. NeurIPS 2026Robust Diffusion Models via Divergence-Induced Weighted Denoising
85. NeurIPS 2026Robust Importance Sampling for Rare Events via Constrained Gaussian Mixtures
86. NeurIPS 2026Sparse Attention as Compact Kernel Regression
87. NeurIPS 2026Spectral Transformer Neural Processes
88. NeurIPS 2026Spherical Bayesian Experimental Design for Active View Selection in 3D Gaussian Splatting
89. NeurIPS 2026StereoSplat: Metric-Scale Novel View Synthesis via Stereo-Grounded Gaussian Splatting
90. NeurIPS 2026Stochastic Heat Diffusion Models
91. NeurIPS 2026Sub-Gaussian Confidence Intervals for Heavy-Tailed Data: Characterizing the Limits of Inference
92. NeurIPS 2026Subprocess-Constrained Markov Decision Processes
93. NeurIPS 2026Sup-Norm Error under Proportional Asymptotics: Phase Transitions under Linear Regression
94. NeurIPS 2026Symplectic Reck: In-Situ Learning of Gaussian Quantum Operations
95. NeurIPS 2026SynGS: Synergistic Continual Learning and Change Detection with Gaussian Splatting
96. NeurIPS 2026The Minimax Rate of Second-Order Calibration
97. NeurIPS 2026The Price of Locality in Label Privacy: Optimal Rates for Classification and Regression
98. NeurIPS 2026The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler
99. NeurIPS 2026Towards Matrix-Parallel and Feature-Scalable 3D Gaussian Splatting Rendering on GPUs
100. NeurIPS 2026TransmissiveGS: Residual-Guided Disentangled Gaussian Splatting for Transmissive Scene Reconstruction and Rendering

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。