GPyTorch’s documentation

Tutorials:

• GPyTorch Regression Tutorial

Examples:

• Basic Usage
• Exact GPs (Regression)
• Exact GPs with Scalable (GPU) Inference
  • BlackBox Matrix-Matrix Inference (BBMM)
  • LancZos Variance Estimates (LOVE)
  • Exact GPs with GPU Acceleration
  • Scalable Posterior Sampling with CIQ
  • Scalable Kernel Approximations
  • Structure-Exploiting Kernels
• Multitask/Multioutput GPs with Exact Inference
  • Multi-output (vector valued functions)
  • Scalar function with multiple tasks
• Variational and Approximate GPs
• Deep GP and Deep Sigma Point Processes
  • Deep GP
  • Deep Sigma Point Processes (DSPP)
• PyTorch NN Integration (Deep Kernel Learning)
• Pyro Integration
  • High-level Pyro Interface (for predictive models)
  • Low-level Pyro Interface (for latent function inference)
• Advanced Usage
  • Batch GPs
  • GPs with Derivatives
  • Variational Fantasization
  • Converting Models to TorchScript

Package Reference

• gpytorch.models
• gpytorch.likelihoods
• gpytorch.kernels
• gpytorch.kernels.keops
• gpytorch.means
• gpytorch.mlls
• gpytorch.metrics
• gpytorch.constraints
• gpytorch.distributions
• gpytorch.priors
• gpytorch.variational
• gpytorch.optim

Settings and Beta Features

• gpytorch.settings
• gpytorch.beta_features

Advanced Package Reference

• gpytorch.Module
• gpytorch.functions
• gpytorch.utils

Indices and tables

• Index

• Module Index

• Search Page

Research references

• Gardner, Jacob R., Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson. “ GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.” In NeurIPS (2018).

• Pleiss, Geoff, Jacob R. Gardner, Kilian Q. Weinberger, and Andrew Gordon Wilson. “Constant-Time Predictive Distributions for Gaussian Processes.” In ICML (2018).

• Gardner, Jacob R., Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, and Andrew Gordon Wilson. “Product Kernel Interpolation for Scalable Gaussian Processes.” In AISTATS (2018).

• Wilson, Andrew G., Zhiting Hu, Ruslan R. Salakhutdinov, and Eric P. Xing. “Stochastic variational deep kernel learning.” In NeurIPS (2016).

• Wilson, Andrew, and Hannes Nickisch. “Kernel interpolation for scalable structured Gaussian processes (KISS-GP).” In ICML (2015).

• Hensman, James, Alexander G. de G. Matthews, and Zoubin Ghahramani. “Scalable variational Gaussian process classification.” In AISTATS (2015).