Strong inductive biases provably prevent harmless interpolation

This repository contains the official code of the ICLR 2023 paper

Strong inductive biases provably prevent harmless interpolation

by Michael Aerni, Marco Milanta, Konstantin Donhauser, Fanny Yang.

Please cite our work and this code as

@inproceedings{Aerni23,
  title={Strong inductive biases provably prevent harmless interpolation},
  author={Michael Aerni and Marco Milanta and Konstantin Donhauser and Fanny Yang},
  booktitle={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2023},
}

Setup and dependencies

Initial setup

Install conda (e.g., Miniconda).
Create an environment via conda env create -f environment.yml InductiveBiasesHarmlessInterpolation (this might take some time).
Copy template.env to .env and update the entries where necessary.

Using the environment

The environment can be enabled and disabled from the command line as follows:

# enable
conda activate InductiveBiasesHarmlessInterpolation

# disable
conda deactivate

Experiments

For each experiment subset, we provide a bash script to run all the different settings, and collect the results via MLFlow. We further provide Jupyter notebooks to plot experiment results and theoretical rates.

Running

Each run_*.sh script in the config/ directory runs all configurations of a corresponding experiment subset. Those scripts exactly reproduce our results without any further required actions. We use Gin Config to configure all settings. Experiment parameters can be modified by either changing a *.gin file or providing bindings explicitly via command line; see the run_*.sh scripts for reference. Finally, all our experiments run on a single consumer GPUs. For the filter size experiments, we use various GPUs with around 10GB of memory, and NVIDIA GeForce RTX 2080 Ti for the rotational invariance experiments.

Evaluation

The Jupyter notebooks in the plots/ directory evaluate experimental runs. Concretely, filter_size.ipynb and rotations.ipynb evaluate all filter size and rotational invariance experiments, respectively, including ablation experiments. Lastly, theory.ipynb plots our theoretical rates in the paper.

Environment variables

We simplify working with environment variables by using the python-dotenv package. Hence, environment variables can be overwritten in an .env file, placed in the root of this repository. The file template.env serves as a template.

Datasets

All filter size experiments use synthetic data that is generated ad-hoc (see data.py). Note that dataset generation may take a long time, hence generated datasets can be cached.

The rotational invariance experiments use the EuroSAT dataset as provided by PyTorch. By default, the experiments store raw EuroSAT data in a datasets/ folder at the root of this repoistory. The location of this directory can be changed via the DATA_DIR environment variable.

michaelaerni / iclr23-InductiveBiasesHarmlessInterpolation