
Latent Space Exploration Using Generative Kernel PCA
Kernel PCA is a powerful feature extractor which recently has seen a ref...
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Towards Deterministic Diverse Subset Sampling
Determinantal point processes (DPPs) are well known models for diverse s...
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Boosting Coteaching with Compression Regularization for Label Noise
In this paper, we study the problem of learning image classification mod...
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Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Commonly, machine learning models minimize an empirical expectation. As ...
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Unsupervised Energybased Outofdistribution Detection using StiefelRestricted Kernel Machine
Detecting outofdistribution (OOD) samples is an essential requirement ...
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Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
We introduce ConstrDRKM, a deep kernel method for the unsupervised lear...
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Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems
Semiparametric regression models are used in several applications which...
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Towards a Unified Quadrature Framework for LargeScale Kernel Machines
In this paper, we develop a quadrature framework for largescale kernel ...
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Kernel regression in high dimension: Refined analysis beyond double descent
In this paper, we provide a precise characterize of generalization prope...
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A Theoretical Framework for Target Propagation
The success of deep learning, a braininspired form of AI, has sparked i...
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Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes
By using the framework of Determinantal Point Processes (DPPs), some the...
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The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
Generative Adversarial Networks (GANs) are performant generative methods...
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Disentangled Representation Learning and Generation with Manifold Optimization
Disentanglement is an enjoyable property in representation learning whic...
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Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces
In this paper, we study the asymptotical properties of least squares reg...
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Generalizing Random Fourier Features via Generalized Measures
We generalize random Fourier features, that usually require kernel funct...
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Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond
Random features is one of the most soughtafter research topics in stati...
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Diversity sampling is an implicit regularization for kernel methods
Kernel methods have achieved very good performance on large scale regres...
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Wasserstein Exponential Kernels
In the context of kernel methods, the similarity between data points is ...
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Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
In the past decade, interest in generative models has grown tremendously...
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Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
In this paper, we propose a fast surrogate leverage weighted sampling st...
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Generative Restricted Kernel Machines
We propose a novel method for estimating generative models based on the ...
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Nyström landmark sampling and regularized Christoffel functions
Selecting diverse and important items from a large set is a problem of i...
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Efficient hinging hyperplanes neural network and its application in nonlinear system identification
In this paper, the efficient hinging hyperplanes (EHH) neural network is...
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Twostage Bestscored Random Forest for Largescale Regression
We propose a novel method designed for largescale regression problems, ...
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Spatiotemporal Stacked LSTM for Temperature Prediction in Weather Forecasting
Long ShortTerm Memory (LSTM) is a wellknown method used widely on sequ...
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Generalization Properties of hyperRKHS and its Application to OutofSample Extensions
Hyperkernels endowed by hyperReproducing Kernel Hilbert Space (hyperR...
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Positive semidefinite embedding for dimensionality reduction and outofsample extensions
In machine learning or statistics, it is often desirable to reduce the d...
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Solving ℓ^pnorm regularization with tensor kernels
In this paper, we discuss how a suitable family of tensor kernels can be...
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Modified FrankWolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers
This work proposes a new algorithm for training a reweighted L2 Support...
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A Statistical Learning Approach to Modal Regression
This paper studies the nonparametric modal regression problem systematic...
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Parallelized Tensor Train Learning of Polynomial Classifiers
In pattern classification, polynomial classifiers are wellstudied metho...
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Convex Formulation for Kernel PCA and its Use in SemiSupervised Learning
In this paper, Kernel PCA is reinterpreted as the solution to a convex o...
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Kernel Density Estimation for Dynamical Systems
We study the density estimation problem with observations generated by c...
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Learning theory estimates with observations from general stationary stochastic processes
This paper investigates the supervised learning problem with observation...
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Generalized support vector regression: duality and tensorkernel representation
In this paper we study the variational problem associated to support vec...
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Fast and Scalable Lasso via Stochastic FrankWolfe Methods with a Convergence Guarantee
FrankWolfe (FW) algorithms have been often proposed over the last few y...
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Pinball Loss Minimization for Onebit Compressive Sensing
The onebit quantization can be implemented by one single comparator, wh...
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Kernel Spectral Clustering and applications
In this chapter we review the main literature related to kernel spectral...
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Higher order Matching Pursuit for Low Rank Tensor Learning
Low rank tensor learning, such as tensor completion and multilinear mult...
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A PARTANAccelerated FrankWolfe Algorithm for LargeScale SVM Classification
FrankWolfe algorithms have recently regained the attention of the Machi...
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Fast Prediction with SVM Models Containing RBF Kernels
We present an approximation scheme for support vector machine models tha...
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A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
We present a novel approach to learn binary classifiers when only positi...
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Johan A. K. Suykens
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Full Professor Katholieke Universiteit Leuven DEPARTMENT OF ELECTRICAL ENGINEERING (ESAT) since 1996, PhD degree in Applied Sciences from the Katholieke Universiteit Leuven 19891995, Visiting Postdoctoral Researcher at the University of California, Berkeley 1996, Postdoctoral Researcher with the Fund for Scientific Research FWO Flanders.