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Code to reproduce the experiments in the paper Minimal Achievable Sufficient Statistic Learning
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experiments
models
scripts
tests
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README.md
__init__.py
environment.yml
start_evaluating.py
start_training.py
utils.py

README.md

Minimal Achievable Sufficient Statistic Learning

What is this?

This code reproduces all experiments in the paper Minimal Achievable Sufficient Statistic Learning.

License

The code in this repo is licensed under the MIT license.

How do I run your code?

Installation

Install the Conda python package manager. Then follow the instructions here using the file environment.yml file in this library's root directory to satisfy the python requirements to run this library's code.

In theory our code is operating-system-agnostic, but we ran all our experiments on Ubuntu Linux, so that's where you're most likely to have installation success.

Tests (optional)

We included as many unit tests as we could. They're in the tests directory, whose directory structure mirrors that of the rest of the library.

You can run them from the library's root directory with python -m unittest.

Running the experiments from the paper

The scripts that run the experiments are in the scripts directory.

Activate your MASS-Learning conda environment, adjust the options in the scripts to suit your machine, and run whatever experiments you like from the library's root directory as a module, e.g. python -m scripts.paper_tables.SmallMLPAccRegUQOOD.

Experiments log their results in the runs directory. You can activate tensorboard to watch their progress.

Once the experiments are done, the scripts in scripts/evaluations or scripts/plotting will consume the logs and give you the results from the paper.

Questions?

Feel free to get in touch with Milan Cvitkovic or any of the other paper authors. We'd love to hear from you!

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