Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning. - Carlos M. Alaíz; Michaël Fanuel; Johan A. K. Suykens (CoRR, 2016)

Magnetic eigenmaps for community detection in directed networks. - Michaël Fanuel; Carlos M. Alaíz; Johan A. K. Suykens (CoRR, 2016)

Magnetic Eigenmaps for Visualization of Directed Networks. - Michaël Fanuel; Ángela Fernández 001; Carlos M. Alaíz; Johan A. K. Suykens (CoRR, 2016)

Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning. - Carlos M. Alaíz; Michaël Fanuel; Johan A. K. Suykens (IEEE Trans. Neural Networks Learn. Syst., 2018)

Towards Deterministic Diverse Subset Sampling. - Joachim Schreurs; Michaël Fanuel; Johan A. K. Suykens (BNAIC/BENELEARN (Selected Papers), 2019)

Robust classification of graph-based data. - Carlos M. Alaíz; Michaël Fanuel; Johan A. K. Suykens (Data Min. Knowl. Discov., 2019)

Wasserstein Exponential Kernels. - Henri De Plaen; Michaël Fanuel; Johan A. K. Suykens (IJCNN, 2020)

The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. - Hannes De Meulemeester; Joachim Schreurs; Michaël Fanuel; Bart De Moor; Johan A. K. Suykens (CoRR, 2020)

Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. - Joachim Schreurs; Michaël Fanuel; Johan A. K. Suykens (CoRR, 2020)

Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (CoRR, 2020)

Recovering Hölder smooth functions from noisy modulo samples. - Michaël Fanuel; Hemant Tyagi (ACSCC, 2021)

Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. - Joachim Schreurs; Hannes De Meulemeester; Michaël Fanuel; Bart De Moor; Johan A. K. Suykens (LOD, 2021)

Nonparametric estimation of continuous DPPs with kernel methods. - Michaël Fanuel; Rémi Bardenet (NeurIPS, 2021)

The Bures Metric for Generative Adversarial Networks. - Hannes De Meulemeester; Joachim Schreurs; Michaël Fanuel; Bart De Moor; Johan A. K. Suykens (ECML/PKDD (2), 2021)

Diversity Sampling is an Implicit Regularization for Kernel Methods. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (SIAM J. Math. Data Sci., 2021)

Sparsification of the regularized magnetic Laplacian with multi-type spanning forests. - Michaël Fanuel; Rémi Bardenet (CoRR, 2022)

Nyström landmark sampling and regularized Christoffel functions. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (Mach. Learn., 2022)

Disentangled Representation Learning and Generation With Manifold Optimization. - Arun Pandey; Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (Neural Comput., 2022)

Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions. - Michaël Fanuel; Antoine Aspeel; Jean-Charles Delvenne; Johan A. K. Suykens (SIAM J. Math. Data Sci., 2022)

Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems. - Michaël Fanuel; Joachim Schreurs; Johan A. K. Suykens (SIAM J. Math. Data Sci., 2022)

Smoothing Complex-Valued Signals on Graphs with Monte-Carlo. - Hugo Jaquard; Michaël Fanuel; Pierre-Olivier Amblard; Rémi Bardenet; Simon Barthelmé; Nicolas Tremblay (ICASSP, 2023)

On sampling determinantal and Pfaffian point processes on a quantum computer. - Rémi Bardenet; Michaël Fanuel; Alexandre Feller (CoRR, 2023)

On the Number of Steps of CyclePopping in Weakly Inconsistent U(1)-Connection Graphs. - Michaël Fanuel; Rémi Bardenet (CoRR, 2024)

Ellipsoidal Embeddings of Graphs. - Michaël Fanuel; Antoine Aspeel; Michael T. Schaub; Jean-Charles Delvenne (SIAM J. Appl. Math., 2025)

Bypassing orthogonalization in the quantum DPP sampler. - Michaël Fanuel; Rémi Bardenet (CoRR, 2025)