Project details for revrand

Logo revrand 1.0.0

by dsteinberg - January 29, 2017, 04:33:54 CET [ Project Homepage BibTeX Download ]

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This library implements various Bayesian linear models (Bayesian linear regression) and generalized 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 generalized linear models (GLMs). We infer all of the parameters in the GLMs using stochastic variational inference [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.

  • A host of decorators for scipy.optimize.minimize and stochastic gradients that enhance the functionality of these optimisers.

[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:
  • 1.0 release!
  • Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
  • Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
  • Random state can be set for all random objects
  • Numerous small improvements to make revrand production ready
  • Final report
  • Documentation improvements
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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