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 244 篇，第 2 / 5 页

51. ICML 2026On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
52. ICML 2026PCGS: Deblurring 3D Gaussian Splatting with Patch Comparison
53. ICML 2026Policy Search via Bayesian Optimization with Temporal Difference Gaussian Processes
54. ICML 2026Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
55. ICML 2026Prior Diffusiveness and Regret in the Linear-Gaussian Bandit
56. ICML 2026Probabilistically-routed Bayesian Additive Spanning Trees for Learning on Constrained Domains
57. ICML 2026ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation
58. ICML 2026Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
59. ICML 2026Reconstruction Outcomes Look Similar but Processes Differ: Improving Context Consistency and Coverage in Graph Masked Auto-Encoder
60. ICML 2026Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes
61. ICML 2026Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
62. ICML 2026Richer Bayesian Last Layers with Subsampled NTK Features
63. ICML 2026S2GS: Streaming Semantic Gaussian Splatting for Online Scene Understanding and Reconstruction
64. ICML 2026Scalable Bayesian Inference for Nonlinear Conservation Laws
65. ICML 2026SE(3)-Equivariant Flow Matching with Gaussian Process Priors for Geometric Trajectory Prediction
66. ICML 2026Sharp Concentration Bounds for Bundle-Valued Statistics on Manifolds
67. ICML 2026Sharp Inequalities between Total Variation and Hellinger Distances for Gaussian Mixtures
68. ICML 2026Signal Strength Estimation in Logistic Regression Using Data Splitting
69. ICML 2026SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
70. ICML 2026Sparse Regression with $\ell_0$ Constraints for $\alpha$-Mixing Time Series: Algorithms and Guarantees
71. ICML 2026Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite $L_p$ Moments
72. ICML 2026Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
73. ICML 2026Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
74. ICML 2026The Value Function Semi-Algebraic Set in Partially Observable Markov Decision Processes
75. ICML 2026TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization
76. ICML 2026Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
77. ICML 2026Token Sample Complexity of Attention
78. ICML 2026Transformed Latent Variable Multi-Output Gaussian Processes
79. ICML 2026Variational inference via Gaussian interacting particles in the Bures-Wasserstein geometry
80. ICML 2025Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
81. ICML 2025BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
82. ICML 2025Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models
83. ICML 2025Determinant Estimation under Memory Constraints and Neural Scaling Laws
84. ICML 2025Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors
85. ICML 2025Distributionally Robust Active Learning for Gaussian Process Regression
86. ICML 2025Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements
87. ICML 2025Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
88. ICML 2025Finite-Time Analysis of Discrete-Time Stochastic Interpolants
89. ICML 2025Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
90. ICML 2025Global Optimization with a Power-Transformed Objective and Gaussian Smoothing
91. ICML 2025Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables
92. ICML 2025Gradient Flow Provably Learns Robust Classifiers for Orthonormal GMMs
93. ICML 2025Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation
94. ICML 2025Gridded Transformer Neural Processes for Spatio-Temporal Data
95. ICML 2025Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance
96. ICML 2025Integration-free Kernels for Equivariant Gaussian Process Modelling
97. ICML 2025Interaction-Aware Gaussian Weighting for Clustered Federated Learning
98. ICML 2025K$^2$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes
99. ICML 2025Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
100. ICML 2025Learning Gaussian DAG Models without Condition Number Bounds

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。