Gaussian Processes papers, 2023–2026

Key findings

• Gaussian Processes went from 154 papers in 2023 to 341 in 2026, roughly 2.2x in three years.
• NeurIPS publishes the most Gaussian Processes work: 372 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with 3D Vision: papers tagged with both rose from 0 in 2023 to 158 in 2026.
• Among the 5 topics under Probabilistic and Bayesian, it ranks #1 by 2026 paper count; the fastest grower in the group, Generative Flows, is up 1.9x versus 2.2x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Gaussian Processes papers

231 papers, page 2 of 5

51. ICLR 2024EasyTPP: Towards Open Benchmarking Temporal Point Processes
52. ICLR 2024Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation
53. ICLR 2024Function-space Parameterization of Neural Networks for Sequential Learning
54. ICLR 2024Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
55. ICLR 2024GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors
56. ICLR 2024Graphical Multioutput Gaussian Process with Attention
57. ICLR 2024Grokking as a First Order Phase Transition in Two Layer Networks
58. ICLR 2024High-dimensional SGD aligns with emerging outlier eigenspaces
59. ICLR 2024HoloNets: Spectral Convolutions do extend to Directed Graphs
60. ICLR 2024Horizon-Free Regret for Linear Markov Decision Processes
61. ICLR 2024Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
62. ICLR 2024Kalman Filter for Online Classification of Non-Stationary Data
63. ICLR 2024Leave-one-out Distinguishability in Machine Learning
64. ICLR 2024Light Schrödinger Bridge
65. ICLR 2024Making Pre-trained Language Models Great on Tabular Prediction
66. ICLR 2024Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes
67. ICLR 2024Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data
68. ICLR 2024On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation
69. ICLR 2024Optimal Sample Complexity for Average Reward Markov Decision Processes
70. ICLR 2024Prediction Error-based Classification for Class-Incremental Learning
71. ICLR 2024Robust Classification via Regression for Learning with Noisy Labels
72. ICLR 2024Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions
73. ICLR 2024Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
74. ICLR 2024Stochastic Gradient Descent for Gaussian Processes Done Right
75. ICLR 2024Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
76. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
77. ICML 2024$f$-Divergence Based Classification: Beyond the Use of Cross-Entropy
78. ICML 2024A Rate-Distortion View of Uncertainty Quantification
79. ICML 2024A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes
80. ICML 2024Amortized Variational Deep Kernel Learning
81. ICML 2024An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
82. ICML 2024Asymptotics of feature learning in two-layer networks after one gradient-step
83. ICML 2024Asymptotics of Learning with Deep Structured (Random) Features
84. ICML 2024Autoencoding Conditional Neural Processes for Representation Learning
85. ICML 2024Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
86. ICML 2024Classification Under Strategic Self-Selection
87. ICML 2024Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation
88. ICML 2024Consistent Adversarially Robust Linear Classification: Non-Parametric Setting
89. ICML 2024Critical feature learning in deep neural networks
90. ICML 2024Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
91. ICML 2024Deep Regression Representation Learning with Topology
92. ICML 2024EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization
93. ICML 2024Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing
94. ICML 2024Efficient Value Iteration for s-rectangular Robust Markov Decision Processes
95. ICML 2024Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic
96. ICML 2024EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
97. ICML 2024First-Order Manifold Data Augmentation for Regression Learning
98. ICML 2024Gaussian Plane-Wave Neural Operator for Electron Density Estimation
99. ICML 2024Gaussian Processes on Cellular Complexes
100. ICML 2024GaussianPro: 3D Gaussian Splatting with Progressive Propagation

FAQ

How many Gaussian Processes papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,049 in total: 154 in 2023, 231 in 2024, 323 in 2025, 341 in 2026.

How many Gaussian Processes papers are at NeurIPS 2026?

NeurIPS 2026 has 106 Gaussian Processes papers, versus 106 the year before.

How many Gaussian Processes papers are at ICLR 2026?

ICLR 2026 has 40 Gaussian Processes papers, versus 41 the year before.

How many Gaussian Processes papers are at ICML 2026?

ICML 2026 has 79 Gaussian Processes papers, versus 53 the year before.

How many Gaussian Processes papers are at CVPR 2026?

CVPR 2026 has 116 Gaussian Processes papers, versus 123 the year before.

Which conference publishes the most Gaussian Processes papers?

NeurIPS, with 372 papers from 2023 to 2026.