27 citations · 29 across the 9 of their papers we have counts for
7 papers · 1 filter
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Semi-parametric regression models are used in several applications which require comprehensibility without sacrificing accuracy. Typical examples are spline interpolation in geophy…
Outlier detection in non-elliptical data by kernel MRCD
Joachim Schreurs, Iwein Vranckx, Mia Hubert +2
The minimum regularized covariance determinant method (MRCD) is a robust estimator for multivariate location and scatter, which detects outliers by fitting a robust covariance matr…
Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
By using the framework of Determinantal Point Processes (DPPs), some theoretical results concerning the interplay between diversity and regularization can be obtained. In this pape…
The Bures Metric for Generative Adversarial Networks
Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel +2
Generative Adversarial Networks (GANs) are performant generative methods yielding high-quality samples. However, under certain circumstances, the training of GANs can lead to mode…
Disentangled Representation Learning and Generation with Manifold Optimization
Arun Pandey, Michael Fanuel, Joachim Schreurs +1
Disentanglement is a useful property in representation learning which increases the interpretability of generative models such as Variational autoencoders (VAE), Generative Adversa…
Diversity sampling is an implicit regularization for kernel methods
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Kernel methods have achieved very good performance on large scale regression and classification problems, by using the Nyström method and preconditioning techniques. The Nyström ap…
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded…