mlsanityData to 7 Oct 2026

GPflow: A Gaussian process library using TensorFlow

arXiv 1610.08733 ↗First posted 27 Oct 2016stat.MLcited by 139

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Abstract (arXiv)

GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware.

Key facts. Cited by 139 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2016.

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Citing papers per year: 2021: 39; 2022: 22; 2023: 26; 2024: 22; 2025: 20; 2026: 10.

139 papers.

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  1. 2021Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challengescited by 143
  2. 2021The Promises and Pitfalls of Deep Kernel Learningcited by 81
  3. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  4. 2021Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learningcited by 38
  5. 2021Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Informationcited by 33
  6. 2021Spatio-Temporal Variational Gaussian Processescited by 32
  7. 2021Deep Neural Networks as Point Estimates for Deep Gaussian Processescited by 26
  8. 2021SigGPDE: Scaling Sparse Gaussian Processes on Sequential Datacited by 22
  9. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
  10. 2021Last Layer Marginal Likelihood for Invariance Learningcited by 21
  11. 2021Periodic Activation Functions Induce Stationaritycited by 21
  12. 2021Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradientscited by 20
38 more
  1. 2021Dual Parameterization of Sparse Variational Gaussian Processescited by 19
  2. 2022Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learningcited by 18
  3. 2022Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posedcited by 16
  4. 2022Markovian Gaussian Process Variational Autoencoderscited by 16
  5. 2023Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimizationcited by 15
  6. 2021Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equationcited by 14
  7. 2023Robust and Conjugate Gaussian Process Regressioncited by 14
  8. 2021Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detectioncited by 13
  9. 2021GPflux: A Library for Deep Gaussian Processescited by 12
  10. 2023Toward Large Kernel Modelscited by 12
  11. 2021Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factorscited by 11
  12. 2021Non-separable Spatio-temporal Graph Kernels via SPDEscited by 11
  13. 2022Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regressioncited by 11
  14. 2023Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowcited by 11
  15. 2023Large-Scale Gaussian Processes via Alternating Projectioncited by 11
  16. 2024Stopping Bayesian Optimization with Probabilistic Regret Boundscited by 11
  17. 2021Sparse Algorithms for Markovian Gaussian Processescited by 10
  18. 2022Scalable First-Order Bayesian Optimization via Structured Automatic Differentiationcited by 10
  19. 2022Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picturecited by 10
  20. 2022Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUScited by 10
  21. 2023Amortized Inference for Gaussian Process Hyperparameters of Structured Kernelscited by 10
  22. 2024The GeometricKernels Package: Heat and Mat'ern Kernels for Geometric Learning on Manifolds, Meshes, and Graphscited by 10
  23. 2021SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processescited by 9
  24. 2023PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidancecited by 9
  25. 2021The Fast Kernel Transformcited by 8
  26. 2022Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transportcited by 8
  27. 2024Epsilon-Greedy Thompson Sampling to Bayesian Optimizationcited by 8
  28. 2024Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximationcited by 8
  29. 2021Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolationcited by 7
  30. 2022Learning Multi-Task Gaussian Process Over Heterogeneous Input Domainscited by 7
  31. 2022Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classificationcited by 7
  32. 2023Constrained Bayesian Optimization Under Partial Observations: Balanced Improvements and Provable Convergencecited by 6
  33. 2022Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Treescited by 5
  34. 2021Gaussian Processes on Hypergraphscited by 4
  35. 2022Scalable Multi-Agent Lab Framework for Lab Optimizationcited by 4
  36. 2022Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Featurecited by 4
  37. 2022Sparse Gaussian Process Hyperparameters: Optimize or Integrate?cited by 4
  38. 2024Multi-fidelity Gaussian process surrogate modeling for regression problems in physicscited by 4

50 most cited of 139 All 139 (JSON)