Michaël Fanuel

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 34

Top Topics

Dimensionality Reduction

Point Processes

Top Venues

SIAM J. Math. Data Sci.

IEEE Trans. Neural Networks Learn. Syst.

Publications

Michaël Fanuel, Rémi Bardenet
On the Number of Steps of CyclePopping in Weakly Inconsistent U(1)-Connection Graphs. CoRR (2024)
Michaël Fanuel, Antoine Aspeel, Michael T. Schaub, Jean-Charles Delvenne
Ellipsoidal embeddings of graphs. CoRR (2024)
Hugo Jaquard, Michaël Fanuel, Pierre-Olivier Amblard, Rémi Bardenet, Simon Barthelmé, Nicolas Tremblay
Smoothing Complex-Valued Signals on Graphs with Monte-Carlo. ICASSP (2023)
Rémi Bardenet, Michaël Fanuel, Alexandre Feller
On sampling determinantal and Pfaffian point processes on a quantum computer. CoRR (2023)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Nyström landmark sampling and regularized Christoffel functions. Mach. Learn. 111 (6) (2022)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. SIAM J. Math. Data Sci. 4 (3) (2022)
Michaël Fanuel, Rémi Bardenet
Sparsification of the regularized magnetic Laplacian with multi-type spanning forests. CoRR (2022)
Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Disentangled Representation Learning and Generation With Manifold Optimization. Neural Comput. 34 (10) (2022)
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) (2022)
Michaël Fanuel, Rémi Bardenet
Nonparametric estimation of continuous DPPs with kernel methods. CoRR (2021)
Michaël Fanuel, Rémi Bardenet
Nonparametric estimation of continuous DPPs with kernel methods. NeurIPS (2021)
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)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Diversity Sampling is an Implicit Regularization for Kernel Methods. SIAM J. Math. Data Sci. 3 (1) (2021)
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Towards Deterministic Diverse Subset Sampling. CoRR (2021)
Michaël Fanuel, Hemant Tyagi
Recovering Hölder smooth functions from noisy modulo samples. ACSCC (2021)
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 (2021)
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)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. CoRR (2020)
Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens
Wasserstein Exponential Kernels. CoRR (2020)
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. CoRR (2020)
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 (2020)
Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens
Wasserstein Exponential Kernels. IJCNN (2020)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Diversity sampling is an implicit regularization for kernel methods. CoRR (2020)
Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Disentangled Representation Learning and Generation with Manifold Optimization. CoRR (2020)
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN (Selected Papers) (2019)
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN (2019)
Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
Robust classification of graph-based data. Data Min. Knowl. Discov. 33 (1) (2019)
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Nyström landmark sampling and regularized Christoffel functions. CoRR (2019)
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) (2018)
Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions. CoRR (2017)
Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning. CoRR (2016)
Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
Robust Classification of Graph-Based Data. CoRR (2016)
Michaël Fanuel, Ángela Fernández, Carlos M. Alaíz, Johan A. K. Suykens
Magnetic Eigenmaps for Visualization of Directed Networks. CoRR (2016)
Michaël Fanuel, Carlos M. Alaíz, Johan A. K. Suykens
Magnetic eigenmaps for community detection in directed networks. CoRR (2016)