MOMoGP

This is the official repository for MOMoGP introduced in Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression by Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, and Kristian Kersting, to be published at UAI 2021.

Setup

This will clone the repo, install a Python virtual env (requires Python 3.6), the required packages, and will download some datasets.

git clone https://github.com/minimrbanana/MOMoGP
./setup.sh

Demos

To illustrate the usage of the code:

source ./venv_momogp/bin/activate
python run_MOMoGP.py --data=parkinsons

"parkinsons" can be replaced with "scm20d" or "wind" or "energy" or "usflight".

Hyperparameters

If not specified, the corresponding hyperparameters are set by default values. If train on CPU, use:

python run_MOMoGP.py --data=parkinsons --cpu

Citation

If you find this code useful in your research, please consider citing:

@inproceedings{yu2021uai_momogps,
  title = {Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression},
  author = {Yu, Zhongjie and Zhu, Mingye and Trapp, Martin and Skryagin, Arseny and Kersting, Kristian},
  booktitle = {Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = {2021}
}

Acknowledgments

This work is supported by the Federal Ministry of Education and Research (BMBF; project "MADESI", FKZ 01IS18043B, and Competence Center for AI and Labour; "kompAKI", FKZ 02L19C150), the Hessian Ministry of Higher Education, Research, Science and the Arts (HMWK; projects "The Third Wave of AI" and "The Adaptive Mind"), the Hessian research priority programme LOEWE within the project "WhiteBox", and the National Research Center for Applied Cybersecurity ATHENE, a joint effort of BMBF and HMWK. M.T. acknowledges funding from the Academy of Finland (grant number 324345).
The code is developed based on the Python implementation of DSMGP from Eugene.

minimrbanana / MOMoGP