高斯过程论文趋势 2023–2026

GP 回归与分类

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,049 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

高斯过程论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
202369274810154
202491366440231
20251064153123323
20261064079116341

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

全部高斯过程论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 372 篇，第 7 / 8 页

  1. NeurIPS 2024Universality in Transfer Learning for Linear Models
  2. NeurIPS 2024VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
  3. NeurIPS 2024WildGaussians: 3D Gaussian Splatting In the Wild
  4. NeurIPS 2023A Bayesian Take on Gaussian Process Networks
  5. NeurIPS 2023Adaptive Linear Estimating Equations
  6. NeurIPS 2023Add and Thin: Diffusion for Temporal Point Processes
  7. NeurIPS 2023Automatic Integration for Spatiotemporal Neural Point Processes
  8. NeurIPS 2023Batch Bayesian Optimization For Replicable Experimental Design
  9. NeurIPS 2023Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability
  10. NeurIPS 2023Bayesian Optimization with Cost-varying Variable Subsets
  11. NeurIPS 2023Beta Diffusion
  12. NeurIPS 2023Beyond Average Return in Markov Decision Processes
  13. NeurIPS 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
  14. NeurIPS 2023Bias in Evaluation Processes: An Optimization-Based Model
  15. NeurIPS 2023Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
  16. NeurIPS 2023CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
  17. NeurIPS 2023Collaboratively Learning Linear Models with Structured Missing Data
  18. NeurIPS 2023Compositional Sculpting of Iterative Generative Processes
  19. NeurIPS 2023Demystifying Softmax Gating Function in Gaussian Mixture of Experts
  20. NeurIPS 2023Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
  21. NeurIPS 2023Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
  22. NeurIPS 2023End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
  23. NeurIPS 2023Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
  24. NeurIPS 2023Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
  25. NeurIPS 2023Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
  26. NeurIPS 2023Failure-Aware Gaussian Process Optimization with Regret Bounds
  27. NeurIPS 2023GAUCHE: A Library for Gaussian Processes in Chemistry
  28. NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
  29. NeurIPS 2023Gaussian Membership Inference Privacy
  30. NeurIPS 2023Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
  31. NeurIPS 2023Geometric Neural Diffusion Processes
  32. NeurIPS 2023GPEX, A Framework For Interpreting Artificial Neural Networks
  33. NeurIPS 2023Graph-Structured Gaussian Processes for Transferable Graph Learning
  34. NeurIPS 2023Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
  35. NeurIPS 2023Implicit Manifold Gaussian Process Regression
  36. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
  37. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
  38. NeurIPS 2023Kernelized Reinforcement Learning with Order Optimal Regret Bounds
  39. NeurIPS 2023Learning Causal Models under Independent Changes
  40. NeurIPS 2023Learning Curves for Deep Structured Gaussian Feature Models
  41. NeurIPS 2023Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
  42. NeurIPS 2023Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
  43. NeurIPS 2023Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
  44. NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
  45. NeurIPS 2023On Differentially Private Sampling from Gaussian and Product Distributions
  46. NeurIPS 2023On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
  47. NeurIPS 2023On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
  48. NeurIPS 2023Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
  49. NeurIPS 2023Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
  50. NeurIPS 2023Policy Gradient for Rectangular Robust Markov Decision Processes

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接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查高斯过程在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "gaussian-processes"}

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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。

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