Official implementation of Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models.

git clone https://github.com/nik-dim/pamal.git && cd pamal

# Method 1: Conda
conda create -n pamal python=3.10
conda activate pamal
pip install -r requirements.txt

# Method 2: Dockerfile
docker build -t pamal .

The experiments are configured using Hydra. Hydra uses a hierarchical structure to define experiments. See here for more details.

The configuration files are in the configs/ directory and are further split into two directories. The first, configs/general/, houses the definitions of methods, optimizers, datasets etc, while the second one, configs/experiment/ makes uses of these definitions to configure the experiments for each dataset. The supported methods are outlined here and the supported datasets here.

For example, we can run experiments on the MultiMNIST dataset as follows:

# run experiment on MultiMNIST using the proposed method (PaMaL) 
python multimnist.py method=pamal

# ...modify the `inner_method` parameter specific to pamal
python multimnist.py method=pamal method.inner_method=ls # or gradnorm or rwa

# run the same experiment for a baseline
python multimnist.py method=ls # or mgda or phn etc

To launch multiple experiments at the same time (e.g. ablation studies), we can use the --multirun (-m) flag. Note that this flag is sometimes problematic in conjunction with Weights&Biases logging. For example, to run the ablation study for the proposed method, we can run:

# launch the previous experiment for multiple seeds
python multimnist.py -m method=pamal seed=0,1,2

Alternatively, check the run.sh script for launching multiple experiments.

This repository supports the methods outlined in the table below. Apart from the proposed method pamal, we support Pareto Front Approximation methods phn and cosmos, as single solution methods, such as rlw, graddrop, autol, rotograd, cagrad, nashmtl, pcgrad, dwa, si, mgda, uw, ls, stl.

In order to select one baseline set method=XXX where XXX is the ticker presented below. For example, to run the ls baseline, run python census.py method=ls.

The repository supports the datasets in the table below. By default, the datasets will be downloaded to the ~/benchmarks/data directory. The root path can be modified in src/datasets/base_data_module.py.

The code is structured similar to PyTorch Lightning. We have used various codebase sources for baselines. For this reason, the API is not consistent for all baselines. Most baselines can be found in src/methods/weight_methods.py. However, graddrop, autol, rotograd, phn and cosmos are implemented in their own specific way. In order to add a new single solution method, check src/methods/weight_methods.py where multiple loss and gradient balancing methods can be found. For multi solution methods check src/trainer/multi_solution_trainer.py.

If you have any questions, please contact me. We plan to merge and unify the baselines, with method-specific callbacks. For the moment, this is the case only for Auto-$\lambda$ in src/utils/callbacks/auto_lambda_callback.py.

The reporsitory makes heavy use of previously developped codebases. Specifically, we use:

1. https://github.com/lorenmt/mtan
2. https://github.com/AvivNavon/nash-mtl
3. https://github.com/AvivNavon/pareto-hypernetworks
4. https://github.com/ruchtem/cosmos
5. https://github.com/adrianjav/rotograd
6. https://github.com/intel-isl/MultiObjectiveOptimization

If you find our work useful, please consider citing our paper:

@inproceedings{
    dimitriadis2023pareto,
    title={Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models},
    author={Dimitriadis, Nikolaos and Frossard, Pascal and Fleuret, Fran{\c{c}}ois},
    booktitle={International Conference on Machine Learning},
    year={2023},
    url={https://arxiv.org/abs/2210.09759}
}

If you have any questions, please contact [email protected].