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.
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
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".
If not specified, the corresponding hyperparameters are set by default values. If train on CPU, use:
python run_MOMoGP.py --data=parkinsons --cpu
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}
}
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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).
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The code is developed based on the Python implementation of DSMGP from Eugene.