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

1,049 papers, page 21 of 21

1001. NeurIPS 2023Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
1002. NeurIPS 2023Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
1003. NeurIPS 2023Failure-Aware Gaussian Process Optimization with Regret Bounds
1004. NeurIPS 2023GAUCHE: A Library for Gaussian Processes in Chemistry
1005. NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
1006. NeurIPS 2023Gaussian Membership Inference Privacy
1007. NeurIPS 2023Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
1008. NeurIPS 2023Geometric Neural Diffusion Processes
1009. NeurIPS 2023GPEX, A Framework For Interpreting Artificial Neural Networks
1010. NeurIPS 2023Graph-Structured Gaussian Processes for Transferable Graph Learning
1011. NeurIPS 2023Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
1012. NeurIPS 2023Implicit Manifold Gaussian Process Regression
1013. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
1014. NeurIPS 2023Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
1015. NeurIPS 2023Kernelized Reinforcement Learning with Order Optimal Regret Bounds
1016. NeurIPS 2023Learning Causal Models under Independent Changes
1017. NeurIPS 2023Learning Curves for Deep Structured Gaussian Feature Models
1018. NeurIPS 2023Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
1019. NeurIPS 2023Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
1020. NeurIPS 2023Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
1021. NeurIPS 2023Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
1022. NeurIPS 2023On Differentially Private Sampling from Gaussian and Product Distributions
1023. NeurIPS 2023On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
1024. NeurIPS 2023On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
1025. NeurIPS 2023Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
1026. NeurIPS 2023Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
1027. NeurIPS 2023Policy Gradient for Rectangular Robust Markov Decision Processes
1028. NeurIPS 2023Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
1029. NeurIPS 2023Practical Equivariances via Relational Conditional Neural Processes
1030. NeurIPS 2023Precise asymptotic generalization for multiclass classification with overparameterized linear models
1031. NeurIPS 2023Probabilistic Exponential Integrators
1032. NeurIPS 2023Regression with Cost-based Rejection
1033. NeurIPS 2023Restart Sampling for Improving Generative Processes
1034. NeurIPS 2023Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
1035. NeurIPS 2023Robust Bayesian Satisficing
1036. NeurIPS 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
1037. NeurIPS 2023Score-based Generative Models with Lévy Processes
1038. NeurIPS 2023Sharp Calibrated Gaussian Processes
1039. NeurIPS 2023SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions
1040. NeurIPS 2023Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression
1041. NeurIPS 2023Survival Permanental Processes for Survival Analysis with Time-Varying Covariates
1042. NeurIPS 2023Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions
1043. NeurIPS 2023The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
1044. NeurIPS 2023Thin and deep Gaussian processes
1045. NeurIPS 2023Uncovering motifs of concurrent signaling across multiple neuronal populations
1046. NeurIPS 2023Uniform Convergence with Square-Root Lipschitz Loss
1047. NeurIPS 2023Variational Gaussian processes for linear inverse problems
1048. NeurIPS 2023Variational Gaussian Processes with Decoupled Conditionals
1049. NeurIPS 2023Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies

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.