A supplementary code for anonymous ICLR 2020 submission.

It learns deep ensembles of oblivious differentiable decision trees on tabular data

• A machine with some CPU (preferably 2+ free cores) and GPU(s)
  • Running without GPU is possible but takes 8-10x as long even on high-end CPUs
  • Our implementation is memory inefficient and may require a lot of GPU memory to converge
• Some popular Linux x64 distribution
  • Tested on Ubuntu16.04, should work fine on any popular linux64 and even MacOS;
  • Windows and x32 systems may require heavy wizardry to run;
  • When in doubt, use Docker, preferably GPU-enabled (i.e. nvidia-docker)

1. Clone or download this repo. cd yourself to it's root directory.
2. Grab or build a working python enviromnent. Anaconda works fine.
3. Install packages from requirements.txt

• It is critical that you use torch >= 1.1, not 1.0 or earlier
• You will also need jupyter or some other way to work with .ipynb files

4. Run jupyter notebook and open a notebook in ./notebooks/

• Before you run the first cell, change %env CUDA_VISIBLE_DEVICES=# to an index that you plan to use.
• The notebook downloads data from dropbox. You will need 1-5Gb of disk space depending on dataset.

We showcase two typical learning scenarios for classification and regression. Please consult the original paper for training details.