52 citations · 114 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne +1
In machine learning or statistics, it is often desirable to reduce the dimensionality of a sample of data points in a high dimensional space . This paper introduces a…
Robust Classification of Graph-Based Data
Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
A graph-based classification method is proposed for semi-supervised learning in the case of Euclidean data and for classification in the case of graph data. Our manifold learning t…
Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning
Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens
In this paper, Kernel PCA is reinterpreted as the solution to a convex optimization problem. Actually, there is a constrained convex problem for each principal component, so that t…