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 using a modified version of the nonparametric variational inference algorithm presented in [4].

• 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] Gershman, S., Hoffman, M., & Blei, D. "Nonparametric variational inference". arXiv preprint arXiv:1206.4665 (2012).

Changes to previous version:

• Main interfaces to algorithms now follow the scikit learn standard.
• Documentation improved.
• Codebase dramatically simplified.
• Per-datum arguments allowed in GLM.

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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