Welcome to Vanguard’s Documentation!

v3.1.0

Vanguard is a high-level wrapper around GPyTorch and aims to provide a user-friendly interface for training and using Gaussian process models. Vanguard’s main objective is to make a variety of more advanced GP techniques in the machine learning literature available for easy use by a non-specialists and specialists alike. Vanguard is designed for modularity to facilitate straightforward combinations of different techniques.

Vanguard was created by GCHQ.

Examples

• Introduction
• Regression
• Classification

Tutorials

• Creating a Decorator

Components

• Base GP models
• Kernels

Controllers

• Base Controller Class
• Vanilla Gaussian GP Controller
• GPs with Input Uncertainty

Decorators

• Compositionally Warped GPs
• Learning Likelihood Noise
• Normalising Inputs
• Variational Inference
• Distributed GPs
• Classification
• Multitask GPs
• Decorator Tools
• Compositional input warping for GPs
• Hierarchical GPs with Bayesian Hyperparameters
• Higher Rank Features
• Disable standard input scaling

Optimisation

• Smart Optimiser
• Learning rate finder
• Learning rate schedulers

Datasets

• Air Passengers Dataset
• Base Datasets
• Bike Dataset
• Synthetic Data

Indices and tables

• Index

• Module Index

• Search Page

References

[Rasmussen06]

C.E. Rasmussen and C.K.I. Williams. Gaussian Processes for Machine Learning. Adaptive computation and machine learning. MIT Press, 2006. ISBN 9780262182539. URL: https://doi.org/10.7551/mitpress/3206.001.0001.

[Jiang_KATS_2022]

Xiaodong Jiang, Sudeep Srivastava, Sourav Chatterjee, Yang Yu, Jeffrey Handler, Peiyi Zhang, Rohan Bopardikar, Dawei Li, Yanjun Lin, Uttam Thakore, Michael Brundage, Ginger Holt, Caner Komurlu, Rakshita Nagalla, Zhichao Wang, Hechao Sun, Peng Gao, Wei Cheung, Jun Gao, Qi Wang, Marius Guerard, Morteza Kazemi, Yulin Chen, Chong Zhou, Sean Lee, Nikolay Laptev, Tihamér Levendovszky, Jake Taylor, Huijun Qian, Jian Zhang, Aida Shoydokova, Trisha Singh, Chengjun Zhu, Zeynep Baz, Christoph Bergmeir, Di Yu, Ahmet Koylan, Kun Jiang, Ploy Temiyasathit, and Emre Yurtbay. Kats. 3 2022. URL: https://github.com/facebookresearch/Kats.

[FanaeeT2013]

Hadi Fanaee-T. Bike Sharing. UCI Machine Learning Repository, 2013. DOI: https://doi.org/10.24432/C5W894.

[Hensman15]

James Hensman, Alexander Matthews, and Zoubin Ghahramani. Scalable variational gaussian process classification. In Guy Lebanon and S. V. N. Vishwanathan, editors, Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, volume 38 of Proceedings of Machine Learning Research, 351–360. San Diego, California, USA, 09–12 May 2015. PMLR. URL: https://proceedings.mlr.press/v38/hensman15.html.

[Titsias09]

Michalis Titsias. Variational learning of inducing variables in sparse gaussian processes. In David van Dyk and Max Welling, editors, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, volume 5 of Proceedings of Machine Learning Research, 567–574. Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA, 16–18 Apr 2009. PMLR. URL: https://proceedings.mlr.press/v5/titsias09a.html.

[Kuss05]

Malte Kuss and Carl Edward Rasmussen. Assessing approximate inference for binary gaussian process classification. J. Mach. Learn. Res., 6:1679–1704, 12 2005. URL: https://dl.acm.org/doi/10.5555/1046920.1194901.

[Wackernagel03]

Hans Wackernagel. Multivariate Geostatistics: An Introduction with Applications. Springer Berlin Heidelberg, 2003. ISBN 9783662052945. URL: https://link.springer.com/book/10.1007/978-3-662-05294-5.

[Milios18]

Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, and Maurizio Filippone. Dirichlet-based gaussian processes for large-scale calibrated classification. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, 6008–6018. Red Hook, NY, USA, 2018. Curran Associates Inc. URL: https://dl.acm.org/doi/10.5555/3327345.3327500.

[MacKenzie14]

Cameron A. MacKenzie, Theodore B. Trafalis, and Kash Barker. A bayesian beta kernel model for binary classification and online learning problems. Statistical Analysis and Data Mining: The ASA Data Science Journal, 7(6):434–449, 2014. URL: https://www.imse.iastate.edu/files/2015/10/MacKenzie-et-al-A-Bayesian-Beta-Kernel-Model-for-Binary-Classification-and-Online-Learning-Problems.pdf, doi:10.1002/sam.11241.

[Owen13]

Art B. Owen. Monte Carlo theory, methods and examples. https://artowen.su.domains/mc/, 2013. URL: https://artowen.su.domains/mc/.

[Mchutchon11]

Andrew Mchutchon and Carl Rasmussen. Gaussian process training with input noise. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 24. Curran Associates, Inc., 2011. URL: https://proceedings.neurips.cc/paper/2011/file/a8e864d04c95572d1aece099af852d0a-Paper.pdf.

[Rios19]

Gonzalo Rios and Felipe Tobar. Compositionally-warped gaussian processes. Neural Networks, 118:235–246, 2019. URL: https://arxiv.org/abs/1906.09665, doi:10.1016/j.neunet.2019.06.012.

[Cheng17]

Ching-An Cheng and Byron Boots. Variational inference for gaussian process models with linear complexity. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL: https://proceedings.neurips.cc/paper/2017/file/f8da71e562ff44a2bc7edf3578c593da-Paper.pdf.

[Deisenroth15]

Marc Deisenroth and Jun Wei Ng. Distributed gaussian processes. In Francis Bach and David Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, 1481–1490. Lille, France, 07–09 Jul 2015. PMLR. URL: https://proceedings.mlr.press/v37/deisenroth15.html.

[Cao14]

Yanshuai Cao and David J. Fleet. Generalized product of experts for automatic and principled fusion of gaussian process predictions. CoRR, 2014. URL: http://arxiv.org/abs/1410.7827, arXiv:1410.7827.

[Liu18]

Haitao Liu, Jianfei Cai, Yi Wang, and Yew Soon Ong. Generalized robust bayesian committee machine for large-scale gaussian process regression. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, 3131–3140. PMLR, 10–15 Jul 2018. URL: https://proceedings.mlr.press/v80/liu18a.html.

[Maddox21]

Wesley Maddox and Geoff Pleiss. Exact gp regression on classification labels. URL: https://docs.gpytorch.ai/en/stable/examples/01_Exact_GPs/GP_Regression_on_Classification_Labels.html.

[Lalchand20]

Vidhi Lalchand and Carl Edward Rasmussen. Approximate inference for fully bayesian gaussian process regression. In Cheng Zhang, Francisco Ruiz, Thang Bui, Adji Bousso Dieng, and Dawen Liang, editors, Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, volume 118 of Proceedings of Machine Learning Research, 1–12. PMLR, 08 Dec 2020. URL: https://proceedings.mlr.press/v118/lalchand20a.html.