Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle

This repository contains the code and data for our preprint "Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle".

Setup

We include a conda environment file but this is honestly overkill. You should only need matplotlib, numpy, pandas, scikit-learn and seaborn. If you insist on a new conda environment, here are the steps:

(Optional) Update conda:

conda update -n base -c defaults conda

Create a conda environment with the required packages:

conda env create --file environment.yml

To activate the environment:

conda activate double_descent

Running

Note: The code was written for simplicity and understandability. Minimizing code duplication was intentionally not a priority.

Double Descent in Linear Regression

To reproduce the double descent and the ablations of each of the three factors, run linear_regression_ablations.py. Note: if you want fast but noisy results, set num_repeats to a smaller number.

Adversarial Training and Test Data in Linear Regression

To study how to create adversarial test examples or adversarial training data, run linear_regression_adversarial.py. Note: if you want fast but noisy results, set num_repeats to a smaller number.

Geometric Intuition for Smallest Non-Zero Singular Value

To understand why small non-zero singular values are likely to appear near the interpolation threshold, run smallest_nonzero_singular_value.py

Double Descent in Polynomial Regression

To build intuition in a setting where the number of parameters needn't equal the number of dimensions, run polynomial_regression.py.

Contributing

Reach out to Rylan Schaeffer at rylanschaeffer@gmail.com. General preferences:

1. Use black to format your code. See here for more information. To install, pip install black. To format the repo, run black . from the root directory.
2. Use type hints as much as possible.
3. Imports should proceed in two blocks: (1) general python libraries, (2) custom python code. Both blocks should be alphabetically ordered.

Authorship

Authors: Rylan Schaeffer, Zachary Robertson, Akhilan Boopathy, Mikail Khona, Kateryna Pistunova, Jason W. Rocks, Ila Rani Fiete, Sanmi Koyejo.