- Basic Usage
- Exact GPs (Regression)
- Exact GPs with Scalable (GPU) Inference
- Multitask/Multioutput GPs with Exact Inference
- Variational and Approximate GPs
- Deep GP and Deep Sigma Point Processes
- PyTorch NN Integration (Deep Kernel Learning)
- Pyro Integration
- Advanced Usage
Indices and tables¶
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).