Michaël Fanuel

Michaël Fanuel

Postdoc in machine learning/applied mathematics, PhD in Physics

Publications

Scholar

Preprints

  1. Fanuel, M., & Bardenet, R. (2024). On the Number of Steps of CyclePopping in Weakly Inconsistent U(1)-Connection Graphs.
    arXiv code
    BibTeX 
                    
    @unpublished{FanBar2024,
  title = {{On the Number of Steps of CyclePopping in Weakly Inconsistent U(1)-Connection Graphs}},
  author = {Fanuel, M. and Bardenet, R.},
  year = {2024},
  arxivid = {2404.14803},
  code = {https://github.com/For-a-few-DPPs-more/MagneticLaplacianSparsifier.jl/tree/counting_steps}
}
  2. Fanuel, M., Aspeel, A., Schaub, M. T., & Delvenne, J.-C. (2024). Ellipsoidal embeddings of graphs.
    arXiv code
    BibTeX 
                    
    @unpublished{FASD2024,
  title = {{Ellipsoidal embeddings of graphs}},
  author = {Fanuel, M. and Aspeel, A. and Schaub, M.T. and Delvenne, J.-C.},
  year = {2024},
  arxivid = {2403.15023},
  code = {https://github.com/mrfanuel/EllipsoidalGraphEmbedding.jl}
}
  3. Fanuel, M., & Bardenet, R. (2022). Sparsification of the regularized magnetic Laplacian thanks to multi-type spanning forests.
    arXiv code
    BibTeX 
                    
    @unpublished{FanBar2022,
  title = {{Sparsification of the regularized magnetic Laplacian thanks to multi-type spanning forests}},
  author = {Fanuel, M. and Bardenet, R.},
  year = {2022},
  arxivid = {2208.14797},
  code = {https://github.com/For-a-few-DPPs-more/MagneticLaplacianSparsifier.jl}
}

Journal articles

  1. Bardenet, R., Fanuel, M., & Feller, A. (2024). On sampling determinantal and Pfaffian point processes on a quantum computer. Journal of Physics A: Mathematical and Theoretical, 57(5), 055202.
    paper arXiv code
    BibTeX 
                    
    @article{BFF2023,
  year = {2024},
  volume = {57},
  number = {5},
  pages = {055202},
  author = {Bardenet, R. and Fanuel, M. and Feller, A.},
  title = {On sampling determinantal and Pfaffian point processes on a quantum computer},
  journal = {Journal of Physics A: Mathematical and Theoretical},
  code = {https://github.com/For-a-few-DPPs-more/quantum-sampling-DPPs},
  arxivid = {2305.15851},
  url = {https://dx.doi.org/10.1088/1751-8121/ad1b75}
}
  2. Fanuel, M., Schreurs, J., & Suykens, J. A. K. (2022). Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. SIAM Journal on Mathematics of Data Science, 4(3), 1171–1190.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2021DPPsemiparam,
  author = {Fanuel, M. and Schreurs, J. and Suykens, J.A.K.},
  title = {Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems},
  journal = {SIAM Journal on Mathematics of Data Science},
  volume = {4},
  number = {3},
  pages = {1171-1190},
  year = {2022},
  arxivid = {2011.06964},
  url = {https://doi.org/10.1137/21M1403977}
}
  3. Fanuel, M., & Tyagi, H. (2022). Denoising modulo samples: k-NN regression and tightness of SDP relaxation. Information and Inference: A Journal of the IMA, 11(2), 637–677.
    paper arXiv code
    BibTeX 
                    
    @article{FanuelTyagi2020Mod1,
  author = {Fanuel, M. and Tyagi, H.},
  title = {Denoising modulo samples: k-NN regression and tightness of SDP relaxation},
  year = {2022},
  arxivid = {2009.04850},
  journal = {Information and Inference: A Journal of the IMA},
  volume = {11},
  number = {2},
  pages = {637-677},
  code = {https://github.com/mrfanuel/denoising-modulo-samples-k-NN-regression},
  url = {https://academic.oup.com/imaiai/advance-article-abstract/doi/10.1093/imaiai/iaab022/6395306}
}
  4. Pandey, A., Fanuel, M., Schreurs, J., & Suykens, J. A. K. (2022). Disentangled Representation Learning and Generation with Manifold Optimization. Neural Computation, 34(10), 2009–2036.
    paper arXiv code
    BibTeX 
                    
    @article{Pandey2020DisentangledRepresentation,
  author = {Pandey, A. and Fanuel, M. and Schreurs, J. and Suykens, J.A.K.},
  title = {Disentangled Representation Learning and Generation with Manifold Optimization},
  journal = {Neural Computation},
  volume = {34},
  number = {10},
  pages = {2009–2036},
  year = {2022},
  arxivid = {2006.07046},
  code = {https://github.com/EigenPandey/Stiefel_Restricted_Kernel_Machine},
  url = {https://doi.org/10.1162/neco_a_01528}
}
  5. Fanuel, M., Aspeel, A., Delvenne, J.-C., & Suykens, J. A. K. (2022). Positive semi-definite embedding for dimensionality reduction and out-of-sample extension. SIAM Journal on Mathematics of Data Science, 4(1), 153–178.
    paper arXiv code
    BibTeX 
                    
    @article{Fanuel2017PSDEmbedding,
  author = {Fanuel, M. and Aspeel, A. and Delvenne, J-C. and Suykens, J.A.K.},
  title = {Positive semi-definite embedding for dimensionality reduction and out-of-sample extension},
  year = {2022},
  arxivid = {1711.07271},
  journal = {SIAM Journal on Mathematics of Data Science},
  volume = {4},
  number = {1},
  pages = {153-178},
  url = {https://epubs.siam.org/doi/abs/10.1137/20M1370653},
  code = {https://github.com/mrfanuel/sdp-embedding}
}
  6. Fanuel, M., Schreurs, J., & Suykens, J. A. K. (2022). Nystroem landmark sampling and regularized Christoffel functions. Machine Learning, 111, 2213–2254.
    paper arXiv code
    BibTeX 
                    
    @article{Fanuel2019NystroemChristoffel,
  author = {Fanuel, M. and Schreurs, J. and Suykens, J.A.K.},
  title = {Nystroem landmark sampling and regularized Christoffel functions},
  year = {2022},
  arxivid = {1905.12346},
  journal = {Machine Learning},
  volume = {111},
  number = {},
  pages = {2213-2254},
  url = {https://doi.org/10.1007/s10994-022-06165-0},
  code = {https://github.com/joachimschreurs/Nystrom-landmark-sampling-and-regularized-Christoffel-functions}
}
  7. Fanuel, M., Schreurs, J., & Suykens, J. A. K. (2021). Diversity sampling is an implicit regularization for kernel methods. SIAM Journal on Mathematics of Data Science, 3(1), 280–297.
    paper arXiv video
    BibTeX 
                    
    @article{Fanuel2020diversity,
  title = {{Diversity sampling is an implicit regularization for kernel methods}},
  author = {Fanuel, M. and Schreurs, J. and Suykens, J.A.K.},
  journal = {SIAM Journal on Mathematics of Data Science},
  volume = {3},
  number = {1},
  pages = {280-297},
  year = {2021},
  url = {https://epubs.siam.org/doi/abs/10.1137/20M1320031?mobileUi=0},
  arxivid = {2002.08616},
  video = {http://www.nonlocal-methods.eu/oneworld/talks/2020/05/04/session-2.html}
}
  8. Alaiz, C. M., Fanuel, M., & Suykens, J. A. K. (2019). Robust Classification of Graph-Based Data. Data Mining and Knowledge Discovery, 33, 230–251.
    paper arXiv
    BibTeX 
                    
    @article{Alaiz2019RobustGraph,
  title = {{Robust Classification of Graph-Based Data}},
  author = {Alaiz, C. M. and Fanuel, M. and Suykens, J. A. K.},
  journal = {Data Mining and Knowledge Discovery},
  volume = {33},
  pages = {230-251},
  year = {2019},
  arxivid = {1612.07141},
  url = {https://link.springer.com/article/10.1007/s10618-018-0603-9}
}
  9. Fanuel, M., & Suykens, J. A. K. (2019). Deformed Laplacians and spectral ranking in directed networks. Applied and Computational Harmonic Analysis, 47(2), 397–422.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2019Deformed,
  title = {{Deformed Laplacians and spectral ranking in directed networks}},
  author = {Fanuel, M. and Suykens, J. A. K.},
  journal = {Applied and Computational Harmonic Analysis},
  volume = {47},
  issue = {2},
  pages = {397-422},
  year = {2019},
  arxivid = {1511.00492},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S1063520317301008}
}
  10. Alaiz, C. M., Fanuel, M., & Suykens, J. A. K. (2018). Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3863–3869.
    paper arXiv
    BibTeX 
                    
    @article{Alaiz2018KPCA,
  title = {{Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning}},
  author = {Alaiz, C. M. and Fanuel, M. and Suykens, J. A. K.},
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  volume = {29},
  issue = {8},
  pages = {3863-3869},
  year = {2018},
  arxivid = {1610.06811},
  url = {https://ieeexplore.ieee.org/abstract/document/7956213?casa_token=mtTAUE5nnrkAAAAA:ByqowPsq0bhOvhb8vPrXcT-7XJGtvG_PsXr9mnSz6PxuAv8kUx8TcM3aOXFaRBpoJPy9xiMLl9o}
}
  11. Fanuel, M., Alaiz, C. M., Fernandez, A., & Suykens, J. A. K. (2018). Magnetic eigenmaps for the visualization of directed networks. Applied and Computational Harmonic Analysis, 44(1), 189–199.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2018MagneticEigenmapsVisualization,
  title = {{Magnetic eigenmaps for the visualization of directed  networks}},
  author = {Fanuel, M. and Alaiz, C. M. and Fernandez, A. and Suykens, J. A. K.},
  journal = {Applied and Computational Harmonic Analysis},
  volume = {44},
  issue = {1},
  pages = {189-199},
  year = {2018},
  arxivid = {1606.08266},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S1063520317300052}
}
  12. Fanuel, M., Alaiz, C. M., & Suykens, J. A. K. (2017). Magnetic eigenmaps for community detection in directed networks. Phys. Rev. E, 95, 022302.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2017MagneticEigenmapsCommunity,
  title = {{Magnetic eigenmaps for community detection in directed networks}},
  author = {Fanuel, M. and Alaiz, C. M. and Suykens, J. A. K.},
  journal = {Phys. Rev. E},
  volume = {95},
  pages = {022302},
  year = {2017},
  arxivid = {1606.07359},
  url = {https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.022302}
}
  13. Fanuel, M., & Govaerts, J. (2014). Non-Perturbative Dynamics, Pair Condensation, Confinement and Dynamical Masses in Massless QED2+1. J. Phys. A: Math. Theor., 47, 405401.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2014QED2,
  title = {{Non-Perturbative Dynamics, Pair Condensation, Confinement and Dynamical Masses in Massless QED2+1}},
  author = {Fanuel, M. and Govaerts, J.},
  journal = {J. Phys. A: Math. Theor.},
  volume = {47},
  pages = {405401},
  year = {2014},
  arxivid = {1405.7230},
  url = {https://iopscience.iop.org/article/10.1088/1751-8113/47/40/405401}
}
  14. Fanuel, M., Govaerts, J., Avossevou, G. Y. H., & Dossa, A. F. (2014). The N = 1 Supersymmetric Wong Equations and the Non-Abelian Landau Problem. J. Phys. A: Math. Theor., 47, 465401.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2014N1SUSY,
  title = {{The N = 1 Supersymmetric Wong Equations and the Non-Abelian Landau Problem}},
  author = {Fanuel, M. and Govaerts, J. and Avossevou, G.Y.H. and Dossa, A.F.},
  journal = {J. Phys. A: Math. Theor.},
  volume = {47},
  pages = {465401},
  year = {2014},
  arxivid = {1405.5335},
  url = {https://iopscience.iop.org/article/10.1088/1751-8113/47/46/465401/meta}
}
  15. Fanuel, M., & Zonetti, S. (2013). Affine Quantization and the Initial Cosmological Singularity. EPL, 10001.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2013AffineQuantization,
  title = {{Affine Quantization and the Initial Cosmological Singularity}},
  author = {Fanuel, M. and Zonetti, S.},
  journal = {EPL},
  pages = {10001},
  year = {2013},
  arxivid = {1203.4936},
  url = {https://iopscience.iop.org/article/10.1209/0295-5075/101/10001}
}
  16. Fanuel, M., & Govaerts, J. (2012). Dressed Fermions, Modular Transformations and Bosonization in the Compactified Schwinger Model. J. Phys. A: Math. Theor., 45, 035401.
    paper arXiv
    BibTeX 
                    
    @article{Fanuel2012QED1,
  title = {{Dressed Fermions, Modular Transformations and Bosonization in the Compactified Schwinger Model}},
  author = {Fanuel, M. and Govaerts, J.},
  journal = {J. Phys. A: Math. Theor.},
  volume = {45},
  pages = {035401},
  year = {2012},
  arxivid = {1108.5039},
  url = {https://iopscience.iop.org/article/10.1088/1751-8113/45/3/035401}
}

Conference papers

  1. Jaquard, H., Fanuel, M., Amblard, P.-O., Bardenet, R., Barthelmé, S., & Tremblay, N. (2023). Smoothing complex-valued signals on Graphs with Monte-Carlo. To Appear at International Conference on Acoustics, Speech and Signal Processing (ICASSP).
    arXiv code
    BibTeX 
                    
    @inproceedings{JFABBT2022,
  title = {{Smoothing complex-valued signals on Graphs with Monte-Carlo}},
  author = {Jaquard, H. and Fanuel, M. and Amblard, P-O. and Bardenet, R. and Barthelmé, S. and Tremblay, N.},
  year = {2023},
  booktitle = {To appear at International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  arxivid = {2210.08014},
  code = {https://gricad-gitlab.univ-grenoble-alpes.fr/tremblan/mtsf_for_graph_smoothing}
}
  2. Fanuel, M., & Tyagi, H. (2021). Recovering Hölder smooth functions from noisy modulo samples. 55th Asilomar Conference on Signals, Systems, and Computers, 857–861.
    arXiv code
    BibTeX 
                    
    @inproceedings{FanuelTyagi2021,
  author = {Fanuel, M. and Tyagi, H.},
  title = {Recovering Hölder smooth functions from noisy modulo samples},
  year = {2021},
  booktitle = {55th Asilomar Conference on Signals, Systems, and Computers},
  pages = {857-861},
  arxivid = {2112.01610},
  code = {https://github.com/mrfanuel/denoising-modulo-samples-local-polynomial-estimator}
}
  3. Fanuel, M., & Bardenet, R. (2021). Nonparametric estimation of continuous DPPs with kernel methods. Advances in Neural Information Processing Systems 34 Proceedings (NeurIPS 2021) .
    paper arXiv code poster
    BibTeX 
                    
    @inproceedings{Fanuel2021NonParametricDPPs,
  author = {Fanuel, M. and Bardenet, R.},
  title = {Nonparametric estimation of continuous DPPs with kernel methods},
  year = {2021},
  booktitle = {Advances in Neural Information Processing Systems 34 proceedings (NeurIPS 2021) },
  arxivid = {2106.14210},
  code = {https://github.com/mrfanuel/LearningContinuousDPPs.jl},
  url = {https://openreview.net/forum?id=MGHO3xLMohC}
}
  4. Schreurs, J., De Meulemeester, H., Fanuel, M., De Moor, B., & Suykens, J. A. K. (2021). Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. The International Conference on Machine Learning, Optimization, and Data Science (LOD 2021).
    paper arXiv code
    BibTeX 
                    
    @inproceedings{Schreurs2021CompleteModeCoverage,
  author = {Schreurs, J. and De Meulemeester, H. and Fanuel, M. and De Moor, B. and Suykens, J.A.K.},
  title = {Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks},
  booktitle = {The International Conference on Machine Learning, Optimization, and Data Science (LOD 2021)},
  pages = {},
  year = {2021},
  arxivid = {2104.02373},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-95470-3_35},
  code = {https://github.com/joachimschreurs/RLS_GAN}
}
  5. De Meulemeester, H., Schreurs, J., Fanuel, M., De Moor, B., & Suykens, J. A. K. (2020). The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021).
    arXiv code
    BibTeX 
                    
    @inproceedings{DeMeulemeester2020BuresGAN,
  author = {De Meulemeester, H. and Schreurs, J. and Fanuel, M. and De Moor, B. and Suykens, J.A.K.},
  title = {The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks},
  booktitle = {The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)},
  pages = {},
  code = {https://github.com/hannesdm/The-Bures-Metric-for-Generative-Adversarial-Networks},
  year = {2020},
  arxivid = {2006.09096}
}
  6. De Plaen, H., Fanuel, M., & Suykens, J. A. K. (2020). Wasserstein exponential kernels. International Joint Conference on Neural Networks (IJCNN 2020), 1–6.
    paper arXiv code
    BibTeX 
                    
    @inproceedings{DePlaen2020WassersteinExp,
  author = {De Plaen, H. and Fanuel, M. and Suykens, J.A.K.},
  title = {Wasserstein exponential kernels},
  booktitle = {International Joint Conference on Neural Networks (IJCNN 2020)},
  year = {2020},
  arxivid = {2002.01878},
  volume = {},
  number = {},
  pages = {1-6},
  url = {https://doi.org/10.1109/IJCNN48605.2020.9207630},
  code = {https://github.com/hdeplaen/Wasserstein_Exponential_Kernels}
}
  7. Schreurs, J., Fanuel, M., & Suykens, J. A. K. (2019). Towards deterministic diverse subset sampling. Artificial Intelligence and Machine Learning (Proc. of the 28th Belgian Dutch Conference on Machine Learning), 137–151.
    paper arXiv
    BibTeX 
                    
    @inproceedings{Schreurs2019TowardsDeterminiticDiverse,
  author = {Schreurs, J. and Fanuel, M. and Suykens, J.A.K.},
  title = {Towards deterministic diverse subset sampling},
  booktitle = {Artificial Intelligence and Machine Learning (Proc. of the 28th Belgian Dutch Conference on Machine Learning)},
  year = {2019},
  pages = {137-151},
  arxivid = {2105.13942},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-65154-1_8}
}

Workshop papers

  1. Schreurs, J., Fanuel, M., & Suykens, J. A. K. (2020). Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes. In ICML 2020 workshop on Negative Dependence and Submodularity, PMLR 119.
    paper arXiv
    BibTeX 
                    
    @misc{Schreurs2020diversity,
  title = {{Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes}},
  author = {Schreurs, J. and Fanuel, M. and Suykens, J.A.K.},
  journal = {ICML 2020 workshop on Negative Dependence and Submodularity, PMLR 119},
  year = {2020},
  url = {https://negative-dependence-in-ml-workshop.lids.mit.edu},
  keywords = {workshop},
  arxivid = {2006.13701}
}

Thesis

  1. Fanuel, M. (2014). Non-perturbative Quantum Electrodynamics in low dimensions [Université catholique de Louvain].
    paper
    BibTeX 
                    
    @thesis{Fanuel2014Thesis,
  author = {Fanuel, M.},
  title = {Non-perturbative Quantum Electrodynamics in low dimensions},
  publisher = {Université catholique de Louvain},
  year = {2014},
  edition = {},
  isbn = {},
  url = {https://dial.uclouvain.be/pr/boreal/object/boreal:150732}
}