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 1,049 篇，第 18 / 21 页

  1. NeurIPS 2024Large Spatial Model: End-to-end Unposed Images to Semantic 3D
  2. NeurIPS 2024Learning from Snapshots of Discrete and Continuous Data Streams
  3. NeurIPS 2024LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS
  4. NeurIPS 2024Linear Regression using Heterogeneous Data Batches
  5. NeurIPS 2024LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
  6. NeurIPS 2024LP-3DGS: Learning to Prune 3D Gaussian Splatting
  7. NeurIPS 2024Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
  8. NeurIPS 2024MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction
  9. NeurIPS 2024Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
  10. NeurIPS 2024Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
  11. NeurIPS 2024MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
  12. NeurIPS 2024NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation
  13. NeurIPS 2024Noise-Aware Differentially Private Regression via Meta-Learning
  14. NeurIPS 2024Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
  15. NeurIPS 2024Non-parametric classification via expand-and-sparsify representation
  16. NeurIPS 2024Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
  17. NeurIPS 2024ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings
  18. NeurIPS 2024On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
  19. NeurIPS 2024Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
  20. NeurIPS 2024Physics-Informed Variational State-Space Gaussian Processes
  21. NeurIPS 2024Piecewise deterministic generative models
  22. NeurIPS 2024Pre-trained Gaussian Processes for Bayesian Optimization
  23. NeurIPS 2024R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction
  24. NeurIPS 2024Random Function Descent
  25. NeurIPS 2024ReGS: Reference-based Controllable Scene Stylization with Gaussian Splatting
  26. NeurIPS 2024Reliable Learning of Halfspaces under Gaussian Marginals
  27. NeurIPS 2024Robust Gaussian Processes via Relevance Pursuit
  28. NeurIPS 2024Robust Sparse Regression with Non-Isotropic Designs
  29. NeurIPS 2024Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
  30. NeurIPS 2024Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
  31. NeurIPS 2024Sample-efficient Bayesian Optimisation Using Known Invariances
  32. NeurIPS 2024Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
  33. NeurIPS 2024Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data
  34. NeurIPS 2024Slight Corruption in Pre-training Data Makes Better Diffusion Models
  35. NeurIPS 2024Small coresets via negative dependence: DPPs, linear statistics, and concentration
  36. NeurIPS 2024Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting
  37. NeurIPS 2024Spectral Learning of Shared Dynamics Between Generalized-Linear Processes
  38. NeurIPS 2024Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
  39. NeurIPS 2024Stopping Bayesian Optimization with Probabilistic Regret Bounds
  40. NeurIPS 2024The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems
  41. NeurIPS 2024Truncated Variance Reduced Value Iteration
  42. NeurIPS 2024Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure
  43. NeurIPS 2024Universality in Transfer Learning for Linear Models
  44. NeurIPS 2024VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
  45. NeurIPS 2024WildGaussians: 3D Gaussian Splatting In the Wild
  46. CVPR 2023DLBD: A Self-Supervised Direct-Learned Binary Descriptor
  47. CVPR 2023Gaussian Label Distribution Learning for Spherical Image Object Detection
  48. CVPR 2023GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task
  49. CVPR 2023Interactive Segmentation As Gaussion Process Classification
  50. CVPR 2023Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning

在 Claude、Cursor 里直接问这个趋势

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"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

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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