mlsanityData to 7 Oct 2026

A Framework for Interdomain and Multioutput Gaussian Processes

arXiv 2003.01115 ↗First posted 2 Mar 2020stat.ML cs.LGcited by 47

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Abstract (arXiv)

One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for small variations in the model or inference. In order to improve the utility of GPs we need a modular system that allows rapid implementation and testing, as seen in the neural network community. We present a mathematical and software framework for scalable approximate inference in GPs, which combines interdomain approximations and multiple outputs. Our framework, implemented in GPflow, provides a unified interface for many existing multioutput models, as well as more recent convolutional structures. This simplifies the creation of deep models with GPs, and we hope that this work will encourage more interest in this approach.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 9; 2023: 7; 2024: 7; 2025: 3; 2026: 5.

47 papers.

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  1. 2022Neural Diffusion Processescited by 50
  2. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  3. 2021Deep Neural Networks as Point Estimates for Deep Gaussian Processescited by 26
  4. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
  5. 2021Last Layer Marginal Likelihood for Invariance Learningcited by 21
  6. 2021Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernelscited by 19
  7. 2021Dual Parameterization of Sparse Variational Gaussian Processescited by 19
  8. 2021Discovering Diverse Athletic Jumping Strategiescited by 15
  9. 2022Safe Active Learning for Multi-Output Gaussian Processescited by 13
  10. 2021GPflux: A Library for Deep Gaussian Processescited by 12
  11. 2021Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factorscited by 11
  12. 2022Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUScited by 10
35 more
  1. 2022Variational Nearest Neighbor Gaussian Processcited by 8
  2. 2024Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inferencecited by 8
  3. 2021Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processescited by 7
  4. 2022Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classificationcited by 7
  5. 2021Hierarchical Inducing Point Gaussian Process for Inter-domain Observationscited by 6
  6. 2021Gaussian Processes on Hypergraphscited by 4
  7. 2022Sparse Gaussian Process Hyperparameters: Optimize or Integrate?cited by 4
  8. 2023Actually Sparse Variational Gaussian Processescited by 4
  9. 2025Graph Random Features for Scalable Gaussian Processescited by 4
  10. 2021Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulationcited by 2
  11. 2021Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowcited by 2
  12. 2021Nonnegative spatial factorizationcited by 2
  13. 2022Nonparametric Multi-shape Modeling with Uncertainty Quantificationcited by 2
  14. 2024Residual Deep Gaussian Processes on Manifoldscited by 2
  15. 2025Recurrent Memory for Online Interdomain Gaussian Processescited by 2
  16. 2025HiGP: A high-performance Python package for Gaussian Processcited by 2
  17. 2021Empirical Models for Multidimensional Regression of Fission Systemscited by 1
  18. 2022Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processescited by 1
  19. 2023Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spacescited by 1
  20. 2023Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processescited by 1
  21. 2023Beyond Intuition, a Framework for Applying GPs to Real-World Datacited by 1
  22. 2023Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processescited by 1
  23. 2024Adaptive RKHS Fourier Features for Compositional Gaussian Process Modelscited by 1
  24. 2024Implementation and Analysis of GPU Algorithms for Vecchia Approximationcited by 1
  25. 2024Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscapecited by 1
  26. 2026Lecture notes on Machine Learning applications for global fitscited by 1
  27. 2026Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problemscited by 1
  28. 2021Gaussian Process for Trajectoriescited by 0
  29. 2021Validating Gaussian Process Models with Simulation-Based Calibrationcited by 0
  30. 2023Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holescited by 0
  31. 2023Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamicscited by 0
  32. 2024Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networkscited by 0
  33. 2026Latent-Constrained Conditional VAEs for Augmenting Large-Scale Climate Ensemblescited by 0
  34. 2026Inverse-Free Sparse Variational Gaussian Processescited by 0
  35. 2026Transformed Latent Variable Multi-Output Gaussian Processescited by 0