27 citations · 29 across the 9 of their papers we have counts for
12 papers
Latent Space Exploration Using Generative Kernel PCA
David Winant, Joachim Schreurs, Johan A. K. Suykens
Kernel PCA is a powerful feature extractor which recently has seen a reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs allow for a representation of ker…
Towards Deterministic Diverse Subset Sampling
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search. In this p…
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel +2
Commonly, machine learning models minimize an empirical expectation. As a result, the trained models typically perform well for the majority of the data but the performance may det…
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…
Generative Restricted Kernel Machines: A Framework for Multi-view Generation and Disentangled Feature Learning
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called…
Nyström landmark sampling and regularized Christoffel functions
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Selecting diverse and important items, called landmarks, from a large set is a problem of interest in machine learning. As a specific example, in order to deal with large training…