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Scalable Variational Gaussian Process Classification

arXiv 1411.2005 ↗First posted 7 Nov 2014stat.MLcited by 226

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Abstract (arXiv)

Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.

Key facts. Cited by 226 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Nov 2014.

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Citing papers per year: 2021: 51; 2022: 34; 2023: 45; 2024: 37; 2025: 35; 2026: 24.

226 papers.

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  1. 2021On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertaintycited by 87
  2. 2021The Promises and Pitfalls of Deep Kernel Learningcited by 81
  3. 2021Personalized Federated Learning with Gaussian Processescited by 76
  4. 2021On the Practicality of Deterministic Epistemic Uncertaintycited by 62
  5. 2023Prediction-Oriented Bayesian Active Learningcited by 60
  6. 2022A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awarenesscited by 49
  7. 2021Spatio-Temporal Variational Gaussian Processescited by 32
  8. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  9. 2021Valid prediction intervals for regression problemscited by 28
  10. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 27
  11. 2021GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learningcited by 26
  12. 2021Deep Neural Networks as Point Estimates for Deep Gaussian Processescited by 26
38 more
  1. 2022Bayesian Active Learning with Fully Bayesian Gaussian Processescited by 26
  2. 2023ClimateSet: A Large-Scale Climate Model Dataset for Machine Learningcited by 25
  3. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  4. 2023qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimizationcited by 23
  5. 2024Amortized Probabilistic Conditioning for Optimization, Simulation and Inferencecited by 22
  6. 2021Last Layer Marginal Likelihood for Invariance Learningcited by 21
  7. 2021Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradientscited by 20
  8. 2021Dual Parameterization of Sparse Variational Gaussian Processescited by 19
  9. 2022Additive Gaussian Processes Revisitedcited by 19
  10. 2025Latent Bayesian Optimization via Autoregressive Normalizing Flowscited by 19
  11. 2021Connections and Equivalences between the Nystr"om Method and Sparse Variational Gaussian Processescited by 17
  12. 2022Interactive Imitation Learning of Bimanual Movement Primitivescited by 17
  13. 2022Markovian Gaussian Process Variational Autoencoderscited by 16
  14. 2021Scaling Gaussian Processes with Derivative Information Using Variational Inferencecited by 15
  15. 2021Large-Scale Learning with Fourier Features and Tensor Decompositionscited by 14
  16. 2021Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equationcited by 14
  17. 2023Interactive Segmentation as Gaussian Process Classificationcited by 14
  18. 2023Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexitycited by 13
  19. 2021GPflux: A Library for Deep Gaussian Processescited by 12
  20. 2021Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guaranteescited by 12
  21. 2021Adaptive Gaussian Processes on Graphs via Spectral Graph Waveletscited by 11
  22. 2022Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regressioncited by 11
  23. 2023Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowcited by 11
  24. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
  25. 2023Uncertainty Estimation for Molecules: Desiderata and Methodscited by 11
  26. 2023Large-Scale Gaussian Processes via Alternating Projectioncited by 11
  27. 2021Sparse Algorithms for Markovian Gaussian Processescited by 10
  28. 2022Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picturecited by 10
  29. 2023Amortized Inference for Gaussian Process Hyperparameters of Structured Kernelscited by 10
  30. 2025Large language models as uncertainty-calibrated optimizers for experimental discoverycited by 10
  31. 2021Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networkscited by 9
  32. 2021SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processescited by 9
  33. 2022Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifoldscited by 9
  34. 2023Fully Bayesian Autoencoders with Latent Sparse Gaussian Processescited by 9
  35. 2025Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Modelscited by 9
  36. 2021Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Featurescited by 8
  37. 2021Scalable Variational Gaussian Processes via Harmonic Kernel Decompositioncited by 8
  38. 2022Scalable Gaussian-process regression and variable selection using Vecchia approximationscited by 8

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