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This repository contains an implementation of the Decoupled Gaussian Process model that decouples the representation of mean and covariance in reproducing kernel Hilbert space.

The details of the model is in the paper: Cheng, Ching-An, and Byron Boots. "Variational Inference for Gaussian Process Models with Linear Complexity." Advances in Neural Information Processing Systems. 2017.

Link to the paper: http://papers.nips.cc/paper/7103-variational-inference-for-gaussian-process-models-with-linear-complexity

This model can be used mainly in two ways:

• through session.run() (detailed in decoupled_gaussian_process_example.py), and
• through tf.estimator.Estimator with model_fn() (defined in decoupled_gaussian_process_model.py).

File decoupled_gaussian_process_example.py provides detailed steps to train and evaluate the model by first building the graph, and then iteratively minimizing the objective function by session.run(train_step). In order to use the model as a layer, you may want to embed the logic of adding bases online and hyperparameters initialization in the graph, so that no initial values for hyperparameters are needed and no need to call model.bases.add_bases() in the train loop anymore.

model_fn() is defined in decoupled_gaussian_process_model.py. We can use the following code to create an tf.estimator.Estimator:

estimator = tf.estimator.Estimator(
    model_fn=decoupled_gaussian_process_model.model_fn,
    model_dir=run_config.model_dir,
    params=hparams,
    config=run_config)

Then the tf.estimator.Estimator can be used with tf.contrib.learn.Experiment, to quickly carry out experiments to compare against other models.