README.md

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning (ICML 2023)

This repository contains the official implementation of (Lachapelle et al., 2023).

Installation

To avoid any conflict with your existing Python setup, it is recommended to work in a virtual environment:

python -m venv venv
source venv/bin/activate

Follow these instructions to install the version of JAX corresponding to your versions of CUDA and CuDNN.

pip install -r requirements.txt
pip install -e .

Experiment (3D Shapes)

To reproduce our disentanglement experiment on 3D Shapes (Figure 4), you can run the following script:

python sparsemeta/main_regression.py \
    --meta_lr 0.001 \
    --num_batches 20000 \
    --rep_norm batch_norm \
    --z_dim 6 \
    --shots 25 \
    --test_shots 25 \
    --use_plam \
    --l1reg 0.3 \
    --outer_l1reg 0.0 \
    --l2reg 1e-07 \
    --outer_l2reg 0.0 \
    --use_ridge_solver \
    --task_mode binomial_gauss \
    --weight_decay 0.0 \
    --maxiter_inner 1000 \
    --inner_solver pcd \
    --dis_eval_every 1000 \
    --no_inner_outer_split \
    --scale_noise 0.1 \
    --z_noise_scale 1.0 \
    --z_dist harder_gauss_0.9 \
    --dataset Regression3DShapes

Citation

If you want to cite our work, please use the following Bibtex entry:

@article{lachapelle2023synergiesmultitask,
    title={{Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning}},
    author={Lachapelle, Sebastien and Deleu, Tristan and Mahajan, Divyat and Mitliagkas, Ioannis and Bengio, Yoshua, and Lacoste-Julien, Simon and Bertrand, Quentin},
    journal={International Conference on Machine Learning (ICML)},
    year={2023}
}