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Optimal Ridge Regularization for Out-of-Distribution Prediction

This repository contains code for reproducing results in the paper Optimal Ridge Regularization for Out-of-Distribution Prediction.

Scripts

The following files are included in this repository:

• Util functions:
  • generate_data.py: A Python script that generates the data set.
  • compute_risk.py: A Python script that computes the empirical and theoretical risks of ridge predictor and ensembles.
  • fixed_point_sol.py: A Python script that computes the fixed-point solutions in $(\lambda,\phi)$.
• Ex1
  • ex1_equiv_lam_min.py: compute the minimum feasible $\lambda$.
  • ex1_opt_ridge.py: compute the in-distribution risk of ridge predictors.
  • ex1_opt_ridge_ood.py: compute the OOD risk of ridge predictors.
• Ex2
  • ex2_MNIST.py: compute the OOD risk with distribution shifts on MNIST datasets.
• Ex3
  • ex3_mono.py: compute the ridge risk at different values of $\lambda$.
  • ex3_MNIST.py: compute the OOD risk at different values of $\lambda$ on MNIST datasets.
• Ex4
  • ex4_equiv_v.py: compute the fixed-point solutions in $(\lambda,\phi)$.
  • ex4_equiv_risk.py: compute the risks of ridge predictors in $(\lambda,\phi)$.
• Ex5: Figure F8
  • ex5_theory_ridge_opt.py: theoretical risk of ridge predictors.
  • ex5_theory_ridgeless.py: theoretical risk of full-ensemble ridgeless predictors.
• Visualization:
  • Plot.ipynb: A Jupyter notebook that visualizes the results.

Computation details

All the experiments are run on Ubuntu 22.04.2 LTS (GNU/Linux 5.15.0-72-generic x86_64) using 12 cores.

The estimated time to run all experiments is roughly less than 2 hours for each script.