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ORCIDMichaël Fanuel
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2020 – today
- 2024
[i19]Michaël Fanuel, Antoine Aspeel, Michael T. Schaub, Jean-Charles Delvenne:
Ellipsoidal embeddings of graphs. CoRR abs/2403.15023 (2024)- [i18]Michaël Fanuel, Rémi Bardenet:
On the Number of Steps of CyclePopping in Weakly Inconsistent U(1)-Connection Graphs. CoRR abs/2404.14803 (2024) - 2023
- [c8]Hugo Jaquard
, Michaël Fanuel, Pierre-Olivier Amblard, Rémi Bardenet, Simon Barthelmé, Nicolas Tremblay:
Smoothing Complex-Valued Signals on Graphs with Monte-Carlo. ICASSP 2023: 1-5 - [i17]Rémi Bardenet, Michaël Fanuel, Alexandre Feller:
On sampling determinantal and Pfaffian point processes on a quantum computer. CoRR abs/2305.15851 (2023) - 2022
- [j7]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Nyström landmark sampling and regularized Christoffel functions. Mach. Learn. 111(6): 2213-2254 (2022) - [j6]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation With Manifold Optimization. Neural Comput. 34(10): 2009-2036 (2022) - [j5]Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens:
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions. SIAM J. Math. Data Sci. 4(1): 153-178 (2022) - [j4]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. SIAM J. Math. Data Sci. 4(3): 1171-1190 (2022) - [i16]Michaël Fanuel, Rémi Bardenet:
Sparsification of the regularized magnetic Laplacian with multi-type spanning forests. CoRR abs/2208.14797 (2022) - 2021
- [j3]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity Sampling is an Implicit Regularization for Kernel Methods. SIAM J. Math. Data Sci. 3(1): 280-297 (2021) - [c7]Michaël Fanuel, Hemant Tyagi:
Recovering Hölder smooth functions from noisy modulo samples. ACSCC 2021: 857-861 - [c6]Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. LOD 2021: 466-480 - [c5]Michaël Fanuel, Rémi Bardenet:
Nonparametric estimation of continuous DPPs with kernel methods. NeurIPS 2021: 24124-24136 - [c4]Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
The Bures Metric for Generative Adversarial Networks. ECML/PKDD (2) 2021: 52-66 - [i15]Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. CoRR abs/2104.02373 (2021) - [i14]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. CoRR abs/2105.13942 (2021) - [i13]Michaël Fanuel, Rémi Bardenet:
Nonparametric estimation of continuous DPPs with kernel methods. CoRR abs/2106.14210 (2021) - 2020
- [c3]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. IJCNN 2020: 1-6 - [i12]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. CoRR abs/2002.01878 (2020) - [i11]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity sampling is an implicit regularization for kernel methods. CoRR abs/2002.08616 (2020) - [i10]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation with Manifold Optimization. CoRR abs/2006.07046 (2020) - [i9]Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. CoRR abs/2006.09096 (2020) - [i8]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. CoRR abs/2006.13701 (2020) - [i7]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. CoRR abs/2011.06964 (2020)
2010 – 2019
- 2019
- [j2]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Robust classification of graph-based data. Data Min. Knowl. Discov. 33(1): 230-251 (2019) - [c2]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN 2019 - [c1]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN (Selected Papers) 2019: 137-151 - [i6]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Nyström landmark sampling and regularized Christoffel functions. CoRR abs/1905.12346 (2019) - 2018
- [j1]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning. IEEE Trans. Neural Networks Learn. Syst. 29(8): 3863-3869 (2018) - 2017
- [i5]Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens:
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions. CoRR abs/1711.07271 (2017) - 2016
- [i4]Michaël Fanuel, Carlos M. Alaíz, Johan A. K. Suykens:
Magnetic eigenmaps for community detection in directed networks. CoRR abs/1606.07359 (2016) - [i3]Michaël Fanuel, Ángela Fernández, Carlos M. Alaíz, Johan A. K. Suykens:
Magnetic Eigenmaps for Visualization of Directed Networks. CoRR abs/1606.08266 (2016) - [i2]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning. CoRR abs/1610.06811 (2016) - [i1]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Robust Classification of Graph-Based Data. CoRR abs/1612.07141 (2016)
Coauthor Index
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