Code submission for 'An improved variational approximate posterior for the deep Wishart process'.

All the experiments in the paper can be run by running the two scripts `jug_uci.py` and `jug_uci_cuda.py`.
For easy parallelization of experiments, we used the excellent `jug` package: https://jug.readthedocs.io/en/latest/.
Therefore, as written, the code should be run using `jug execute jug_uci.py` and similarly for `jug_uci_cuda.py`.
If you do not wish to use jug, simply comment out `import jug` and the `@jug.TaskGenerator` decorator in `uci.py`, and the two scripts should run as normal.

`jug_uci.py` runs the experiment for the smaller UCI datasets, while `jug_uci_cuda.py` runs them for the larger datasets.
Results are written as csv files to the `uci_results` directory, with one file for each combination of split and model.

We provide the UCI datasets we used; these were originally taken from https://github.com/yaringal/DropoutUncertaintyExps/tree/master/UCI_Datasets

Code was run with python 3.6.9 and torch version 1.7.0.
