Description:

This library implements various Bayesian linear models (Bayesian linear regression) and generalised linear models. A few features of this library are:

• A fancy basis functions/feature composition framework for combining basis functions like radial basis function, sigmoidal basis functions, polynomial basis functions etc.

• Basis functions that can be used to approximate Gaussian processes with shift invariant covariance functions (e.g. square exponential) when used with linear models [1], [2], [3].

• Non-Gaussian likelihoods with Bayesian generalised linear models (GLMs). We infer all of the parameters in the GLMs using auto-encoding variational Bayes [4], and we approximate the posterior over the weights with a mixture of Gaussians, like [5].

• Large scale learning using stochastic gradient descent (Adam, AdaDelta and more).

• Scikit Learn compatibility, i.e. usable with pipelines.

[1] Yang, Z., Smola, A. J., Song, L., & Wilson, A. G. "A la Carte -- Learning Fast Kernels". Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, pp. 1098-1106, 2015.

[2] Le, Q., Sarlos, T., & Smola, A. "Fastfood-approximating kernel expansions in loglinear time." Proceedings of the international conference on machine learning. 2013.

[3] Rahimi, A., & Recht, B. "Random features for large-scale kernel machines." Advances in neural information processing systems. 2007.

[4] Kingma, D. P., & Welling, M. "Auto-encoding variational Bayes". Proceedings of the 2nd International Conference on Learning Representations (ICLR). 2014.

[5] Gershman, S., Hoffman, M., & Blei, D. "Nonparametric variational inference". Proceedings of the international conference on machine learning. 2012.

Changes to previous version:

• The GLM now uses Auto-encoding variational Bayes for inference as opposed to nonparametric variational inference. This substantially improves performance and simplifies the codebase.
• Many bugfixes.

BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Numpy

Tags: Stochastic Gradient Descent, Large Scale Learning, Nonparametric Bayes, Nonlinear Regression, Gaussian Processes, Generalized Linear Models, Spark, Fast Food, Random Features

Archive: download here

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