mlsanityData to 14 Oct 2026

Random Feature Expansions for Deep Gaussian Processes

arXiv 1610.04386 ↗First posted 14 Oct 2016stat.ML stat.COcited by 32

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Abstract (arXiv)

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work, we introduce a novel formulation of DGPs based on random feature expansions that we train using stochastic variational inference. This yields a practical learning framework which significantly advances the state-of-the-art in inference for DGPs, and enables accurate quantification of uncertainty. We extensively showcase the scalability and performance of our proposal on several datasets with up to 8 million observations, and various DGP architectures with up to 30 hidden layers.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Oct 2016.

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Citing papers per year: 2021: 10; 2022: 4; 2023: 9; 2024: 4; 2025: 2; 2026: 3.

32 papers.

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  1. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  2. 2021Deep Neural Networks as Point Estimates for Deep Gaussian Processescited by 26
  3. 2021The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspectivecited by 21
  4. 2022Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priorscited by 9
  5. 2021Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Featurescited by 8
  6. 2022Deep Variational Implicit Processescited by 8
  7. 2023A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processescited by 8
  8. 2023Sparse Variational Student-t Processescited by 8
  9. 2023Free-Form Variational Inference for Gaussian Process State-Space Modelscited by 7
  10. 2021A Sparse Expansion For Deep Gaussian Processescited by 5
  11. 2023Sequential Estimation of Gaussian Process-based Deep State-Space Modelscited by 5
  12. 2023On permutation symmetries in Bayesian neural network posteriors: a variational perspectivecited by 4
20 more
  1. 2024Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problemcited by 4
  2. 2021Convolutional Normalizing Flows for Deep Gaussian Processescited by 2
  3. 2023Gaussian Process-Gated Hierarchical Mixtures of Expertscited by 2
  4. 2023Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regressioncited by 2
  5. 2024Sparse Variational Student-t Processes for Heavy-tailed Modelingcited by 2
  6. 2025From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximationcited by 2
  7. 2026Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guaranteescited by 2
  8. 2021Explaining Deep Tractable Probabilistic Models: The sum-product network casecited by 1
  9. 2022On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernelcited by 1
  10. 2023Bayesian inference with finitely wide neural networkscited by 1
  11. 2023Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learningcited by 1
  12. 2024Adaptive RKHS Fourier Features for Compositional Gaussian Process Modelscited by 1
  13. 2025Sparse Techniques for Regression in Deep Gaussian Processescited by 1
  14. 2021Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regressioncited by 0
  15. 2021Subset-of-Data Variational Inference for Deep Gaussian-Processes Regressioncited by 0
  16. 2021How Good are Low-Rank Approximations in Gaussian Process Regression?cited by 0
  17. 2022Locally Smoothed Gaussian Process Regressioncited by 0
  18. 2024Stein Random Feature Regressioncited by 0
  19. 2026SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learningcited by 0
  20. 2026How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPscited by 0