GPy

GPy is a Gaussian Process (GP) framework written in python, from the Sheffield machine learning group. Gaussian processes underpin range of modern machine learning algorithms. In GPy, we’ve used python to implement a range of machine learning algorithms based on GPs. GPy is available under the BSD 3-clause license.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 24 articles )

Showing results 1 to 20 of 24.
Sorted by year (citations)

1 2 next

  1. Burt, David R.; Rasmussen, Carl Edward; van der Wilk, Mark: Convergence of sparse variational inference in Gaussian processes regression (2020)
  2. Kast, Mariella; Guo, Mengwu; Hesthaven, Jan S.: A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems (2020)
  3. Lee, Taeksang; Bilionis, Ilias; Buganza Tepole, Adrian: Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression (2020)
  4. Lu, Xuefei; Rudi, Alessandro; Borgonovo, Emanuele; Rosasco, Lorenzo: Faster Kriging: facing high-dimensional simulators (2020)
  5. Razaaly, Nassim; Persico, Giacomo; Gori, Giulio; Congedo, Pietro Marco: Quantile-based robust optimization of a supersonic nozzle for organic rankine cycle turbines (2020)
  6. Schürch, Manuel; Azzimonti, Dario; Benavoli, Alessio; Zaffalon, Marco: Recursive estimation for sparse Gaussian process regression (2020)
  7. Dias, Mafalda; Frazer, Jonathan; Westphal, Alexander: Inflation as an information bottleneck: a strategy for identifying universality classes and making robust predictions (2019)
  8. Lomelí, M.; Rowland, M.; Gretton, A.; Ghahramani, Z.: Antithetic and Monte Carlo kernel estimators for partial rankings (2019)
  9. Vernon, Ian; Jackson, Samuel E.; Cumming, Jonathan A.: Known boundary emulation of complex computer models (2019)
  10. Zhang, Michael Minyi; Williamson, Sinead A.: Embarrassingly parallel inference for Gaussian processes (2019)
  11. Alaa, Ahmed M.; van der Schaar, Mihaela: A hidden absorbing semi-Markov model for informatively censored temporal data: learning and inference (2018)
  12. Antoine Cully; Konstantinos Chatzilygeroudis; Federico Allocati; Jean-Baptiste Mouret: Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization (2018) not zbMATH
  13. Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.: Comparison of Gaussian process modeling software (2018)
  14. Nguyen, Thi Nhat Anh; Bouzerdoum, Abdesselam; Phung, Son Lam: Stochastic variational hierarchical mixture of sparse Gaussian processes for regression (2018)
  15. Razaaly, Nassim; Congedo, Pietro Marco: Novel algorithm using active metamodel learning and importance sampling: application to multiple failure regions of low probability (2018)
  16. Schulz, Eric; Speekenbrink, Maarten; Krause, Andreas: A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (2018)
  17. Simon Olofsson; Ruth Misener: GPdoemd: a python package for design of experiments for model discrimination (2018) arXiv
  18. Matthews, Alexander G. De G.; van der Wilk, Mark; Nickson, Tom; Fujii, Keisuke; Boukouvalas, Alexis; León-Villagrá, Pablo; Ghahramani, Zoubin; Hensman, James: GPflow: a Gaussian process library using TensorFlow (2017)
  19. Perdikaris, P.; Raissi, M.; Damianou, A.; Lawrence, N. D.; Karniadakis, G. E.: Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling (2017)
  20. Damianou, Andreas C.; Titsias, Michalis K.; Lawrence, Neil D.: Variational inference for latent variables and uncertain inputs in Gaussian processes (2016)

1 2 next