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Gaussian Processes for Big Data

arXiv 1309.6835 ↗First posted 26 Sep 2013cs.LG stat.MLcited by 377

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Abstract (arXiv)

We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari- ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our ap- proach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.

Key facts. Cited by 377 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2013.

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Citing papers per year: 2020: 1; 2021: 71; 2022: 77; 2023: 63; 2024: 59; 2025: 64; 2026: 42.

377 papers.

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  1. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  2. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 99
  3. 2021The Bayesian Learning Rulecited by 95
  4. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  5. 2020Active Preference-Based Gaussian Process Regression for Reward Learningcited by 70
  6. 2024Variational Bayesian Last Layerscited by 67
  7. 2021Bayesian Learning via Neural Schr"odinger-F"ollmer Flowscited by 56
  8. 2022Bayesian Model Selection, the Marginal Likelihood, and Generalizationcited by 56
  9. 2021Optimal Order Simple Regret for Gaussian Process Banditscited by 52
  10. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  11. 2021Spatio-Temporal Variational Gaussian Processescited by 32
  12. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
38 more
  1. 2022Discovering Many Diverse Solutions with Bayesian Optimizationcited by 29
  2. 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descentcited by 27
  3. 2021Deep Neural Networks as Point Estimates for Deep Gaussian Processescited by 26
  4. 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
  5. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  6. 2022Posterior and Computational Uncertainty in Gaussian Processescited by 23
  7. 2021Preconditioning for Scalable Gaussian Process Hyperparameter Optimizationcited by 22
  8. 2023Calibrating Transformers via Sparse Gaussian Processescited by 21
  9. 2022AutoIP: A United Framework to Integrate Physics into Gaussian Processescited by 20
  10. 2022Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learningcited by 20
  11. 2022Multi-Fidelity Cost-Aware Bayesian Optimizationcited by 20
  12. 2021Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernelscited by 19
  13. 2021Dual Parameterization of Sparse Variational Gaussian Processescited by 19
  14. 2021Kernel Interpolation for Scalable Online Gaussian Processescited by 18
  15. 2021BOiLS: Bayesian Optimisation for Logic Synthesiscited by 18
  16. 2022Theoretical characterization of uncertainty in high-dimensional linear classificationcited by 18
  17. 2022Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learningcited by 18
  18. 2022Sampling-based inference for large linear models, with application to linearised Laplacecited by 18
  19. 2024Operator Learning with Gaussian Processescited by 18
  20. 2021Connections and Equivalences between the Nystr"om Method and Sparse Variational Gaussian Processescited by 17
  21. 2023Variational Linearized Laplace Approximation for Bayesian Deep Learningcited by 17
  22. 2023Data-Driven Design for Metamaterials and Multiscale Systems: A Reviewcited by 17
  23. 2022Markovian Gaussian Process Variational Autoencoderscited by 16
  24. 2023The Memory Perturbation Equation: Understanding Model's Sensitivity to Datacited by 16
  25. 2023Stochastic Gradient Descent for Gaussian Processes Done Rightcited by 16
  26. 2021Bias-Free Scalable Gaussian Processes via Randomized Truncationscited by 15
  27. 2021Variational multiple shooting for Bayesian ODEs with Gaussian processescited by 15
  28. 2021Scaling Gaussian Processes with Derivative Information Using Variational Inferencecited by 15
  29. 2022Statistical Deep Learning for Spatial and Spatio-Temporal Datacited by 15
  30. 2023Tanimoto Random Features for Scalable Molecular Machine Learningcited by 15
  31. 2021Large-Scale Learning with Fourier Features and Tensor Decompositionscited by 14
  32. 2022Environmental Sensor Placement with Convolutional Gaussian Neural Processescited by 14
  33. 2023Robust and Conjugate Gaussian Process Regressioncited by 14
  34. 2021Grey-box models for wave loading predictioncited by 13
  35. 2021Deep Gaussian Processes: A Surveycited by 13
  36. 2021Function-space Inference with Sparse Implicit Processescited by 13
  37. 2022Partitioned Variational Inference: A Framework for Probabilistic Federated Learningcited by 13
  38. 2022Safe Active Learning for Multi-Output Gaussian Processescited by 13

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