Erdős goes neural
@inproceedings{Karalias2020Erdos,
title={Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs},
author={Nikos Karalias and Andreas Loukas},
booktitle={Neural Information Processing Systems},
year={2020},
series={NeurIPS},
url={https://arxiv.org/abs/2006.10643}
}
How hard is to distinguish graphs with GNN
@inproceedings{Loukas2020How,
title={How hard is to distinguish graphs with graph neural networks?},
author={Andreas Loukas},
booktitle={Neural Information Processing Systems},
year={2020},
series={NeurIPS},
url={https://arxiv.org/abs/2005.06649}
}
Structural message passing
@inproceedings{Vignac2020Building,
title={Building powerful and equivariant graph neural networks with structural message-passing},
author={Clement Vignac and Andreas Loukas and Pascal Frossard},
booktitle={Neural Information Processing Systems},
year={2020},
series={NeurIPS},
url={https://arxiv.org/abs/2006.15107}
}
What graph neural networks cannot learn
@inproceedings{Loukas2020What,
title={What graph neural networks cannot learn: depth vs width},
author={Andreas Loukas},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=B1l2bp4YwS}
}
On the relationship between self-attention and convolution
@inproceedings{Cordonnier2020On,
title={On the Relationship between Self-Attention and Convolutional Layers},
author={Jean-Baptiste Cordonnier and Andreas Loukas and Martin Jaggi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=HJlnC1rKPB}
}
Extrapolating Paths with Graph Neural Networks
@inproceedings{ijcai2019-303,
title={Extrapolating Paths with Graph Neural Networks},
author={Cordonnier, Jean-Baptiste and Loukas, Andreas},
booktitle={Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI-19}},
publisher={International Joint Conferences on Artificial Intelligence Organization},
pages={2187--2194},
year={2019},
month={7},
doi={10.24963/ijcai.2019/303},
url={https://doi.org/10.24963/ijcai.2019/303},
}
Graph reduction with spectral and cut guarantees
@article{JMLR:v20:18-680,
author={Andreas Loukas},
title={Graph Reduction with Spectral and Cut Guarantees},
journal={Journal of Machine Learning Research},
year={2019},
volume={20},
number={116},
pages={1-42},
url = {http://jmlr.org/papers/v20/18-680.html}
}
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?
@inproceedings{pmlr-v70-loukas17a,
title={How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?},
author={Andreas Loukas},
booktitle={Proceedings of the 34th International Conference on Machine Learning},
pages={2228--2237},
year={2017},
volume={70},
series={Proceedings of Machine Learning Research},
publisher = {PMLR}
}
A Time-Vertex Signal Processing Framework
@article{Grassi:228233,
title={A Time-Vertex Signal Processing Framework},
author = {Grassi, Francesco and Loukas, Andreas and Perraudin, Nathanaël and Ricaud, Benjamin},
publisher = {Institute of Electrical and Electronics Engineers},
journal = {IEEE Transactions on Signal Processing},
number = {3},
volume = {66},
pages = {817-829},
year = {2018},
url = {http://infoscience.epfl.ch/record/228233},
doi = {10.1109/TSP.2017.2775589}
}
Stationary time-vertex signal processing
@article{loukas2019stationary,
title={Stationary time-vertex signal processing},
author={Loukas, Andreas and Perraudin, Nathana{\"e}l},
journal={EURASIP Journal on Advances in Signal Processing},
volume={2019},
number={1},
pages={36},
year={2019},
publisher={Springer}
}
Autoregressive Moving Average Graph Filtering
@ARTICLE{7581108,
author={Elvin {Isufi} and Andreas {Loukas} and Andrea {Simonetto} and Geert {Leus}},
journal={IEEE Transactions on Signal Processing},
title={Autoregressive Moving Average Graph Filtering},
year={2017},
volume={65},
number={2},
pages={274-288},
doi={10.1109/TSP.2016.2614793},
ISSN={1941-0476}
}