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 154 篇，第 3 / 4 页

  1. NeurIPS 2023Demystifying Softmax Gating Function in Gaussian Mixture of Experts
  2. NeurIPS 2023Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
  3. NeurIPS 2023Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
  4. NeurIPS 2023End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
  5. NeurIPS 2023Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
  6. NeurIPS 2023Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
  7. NeurIPS 2023Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
  8. NeurIPS 2023Failure-Aware Gaussian Process Optimization with Regret Bounds
  9. NeurIPS 2023GAUCHE: A Library for Gaussian Processes in Chemistry
  10. NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
  11. NeurIPS 2023Gaussian Membership Inference Privacy
  12. NeurIPS 2023Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
  13. NeurIPS 2023Geometric Neural Diffusion Processes
  14. NeurIPS 2023GPEX, A Framework For Interpreting Artificial Neural Networks
  15. NeurIPS 2023Graph-Structured Gaussian Processes for Transferable Graph Learning
  16. NeurIPS 2023Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
  17. NeurIPS 2023Implicit Manifold Gaussian Process Regression
  18. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
  19. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
  20. NeurIPS 2023Kernelized Reinforcement Learning with Order Optimal Regret Bounds
  21. NeurIPS 2023Learning Causal Models under Independent Changes
  22. NeurIPS 2023Learning Curves for Deep Structured Gaussian Feature Models
  23. NeurIPS 2023Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
  24. NeurIPS 2023Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
  25. NeurIPS 2023Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
  26. NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
  27. NeurIPS 2023On Differentially Private Sampling from Gaussian and Product Distributions
  28. NeurIPS 2023On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
  29. NeurIPS 2023On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
  30. NeurIPS 2023Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
  31. NeurIPS 2023Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
  32. NeurIPS 2023Policy Gradient for Rectangular Robust Markov Decision Processes
  33. NeurIPS 2023Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
  34. NeurIPS 2023Practical Equivariances via Relational Conditional Neural Processes
  35. NeurIPS 2023Precise asymptotic generalization for multiclass classification with overparameterized linear models
  36. NeurIPS 2023Probabilistic Exponential Integrators
  37. NeurIPS 2023Regression with Cost-based Rejection
  38. NeurIPS 2023Restart Sampling for Improving Generative Processes
  39. NeurIPS 2023Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
  40. NeurIPS 2023Robust Bayesian Satisficing
  41. NeurIPS 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
  42. NeurIPS 2023Score-based Generative Models with Lévy Processes
  43. NeurIPS 2023Sharp Calibrated Gaussian Processes
  44. NeurIPS 2023SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions
  45. NeurIPS 2023Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression
  46. NeurIPS 2023Survival Permanental Processes for Survival Analysis with Time-Varying Covariates
  47. NeurIPS 2023Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions
  48. NeurIPS 2023The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
  49. NeurIPS 2023Thin and deep Gaussian processes
  50. NeurIPS 2023Uncovering motifs of concurrent signaling across multiple neuronal populations

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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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