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

144 papers, page 3 of 3

101. ICLR 2024Horizon-Free Regret for Linear Markov Decision Processes
102. ICLR 2024Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
103. ICLR 2024Kalman Filter for Online Classification of Non-Stationary Data
104. ICLR 2024Leave-one-out Distinguishability in Machine Learning
105. ICLR 2024Light Schrödinger Bridge
106. ICLR 2024Making Pre-trained Language Models Great on Tabular Prediction
107. ICLR 2024Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes
108. ICLR 2024Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data
109. ICLR 2024On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation
110. ICLR 2024Optimal Sample Complexity for Average Reward Markov Decision Processes
111. ICLR 2024Prediction Error-based Classification for Class-Incremental Learning
112. ICLR 2024Robust Classification via Regression for Learning with Noisy Labels
113. ICLR 2024Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions
114. ICLR 2024Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
115. ICLR 2024Stochastic Gradient Descent for Gaussian Processes Done Right
116. ICLR 2024Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
117. ICLR 2024Uncertainty Quantification via Stable Distribution Propagation
118. ICLR 2023A Kernel Perspective of Skip Connections in Convolutional Networks
119. ICLR 2023Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
120. ICLR 2023Autoregressive Conditional Neural Processes
121. ICLR 2023Bridge the Inference Gaps of Neural Processes via Expectation Maximization
122. ICLR 2023Calibrating Transformers via Sparse Gaussian Processes
123. ICLR 2023Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?
124. ICLR 2023Deep Variational Implicit Processes
125. ICLR 2023Gradient Boosting Performs Gaussian Process Inference
126. ICLR 2023Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
127. ICLR 2023Gray-Box Gaussian Processes for Automated Reinforcement Learning
128. ICLR 2023Improving Deep Regression with Ordinal Entropy
129. ICLR 2023Individual Privacy Accounting with Gaussian Differential Privacy
130. ICLR 2023Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
131. ICLR 2023Latent Bottlenecked Attentive Neural Processes
132. ICLR 2023Learning Adversarial Linear Mixture Markov Decision Processes with Bandit Feedback and Unknown Transition
133. ICLR 2023Learning Label Encodings for Deep Regression
134. ICLR 2023Martingale Posterior Neural Processes
135. ICLR 2023Meta Temporal Point Processes
136. ICLR 2023Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
137. ICLR 2023Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics
138. ICLR 2023Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
139. ICLR 2023Semi-Parametric Inducing Point Networks and Neural Processes
140. ICLR 2023SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning
141. ICLR 2023Spatio-temporal point processes with deep non-stationary kernels
142. ICLR 2023Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning
143. ICLR 2023Versatile Neural Processes for Learning Implicit Neural Representations
144. ICLR 2023VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis

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.