This repository contains the code for reproducing figures and results in the paper ``Provable Continual Learning via Sketched Jacobian Approximations''.

The following Python libraries are required to run the code in this repository:

numpy
jupyter
torch
torchvision
scipy

and can be installed with pip install -r requirements.txt.

All figures in the paper can be reproduced by running the respective notebooks as indicated below:

Figure 1: Sequential learning on the MNIST permutation problem for a neural network and for the random feature model can be reproduced by running the notebooks continual_learning_mnist_permutation_NN and continual_learning_mnist_permutation_random_features.

Figure 2: Sequential learning to classify pairs of MNIST digits can be reproduced by running the continual_learning_mnist_incremental_random_features notebook.

Theorem 4: The risk for the worst case construction is computed in the notebook continual_learning_toy_example.

@inproceedings{,
    author    = {Reinhard Heckel},
    title     = {Provable Continual Learning via Sketched Jacobian Approximations},
    booktitle   = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
    year      = {2022}
}

All files are provided under the terms of the Apache License, Version 2.0.