Suykens, Johan A. K.

24 publications

AAAI 2024 Unsupervised Neighborhood Propagation Kernel Layers for Semi-Supervised Node Classification Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens

CVPR 2023 Unbalanced Optimal Transport: A Unified Framework for Object Detection Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool

MLJ 2022 Nyström Landmark Sampling and Regularized Christoffel Functions Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens

MLJ 2021 Analysis of Regularized Least-Squares in Reproducing Kernel Kreĭn Spaces Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens

CVPRW 2021 Boosting Co-Teaching with Compression Regularization for Label Noise Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens

JMLR 2021 Generalization Properties of Hyper-RKHS and Its Applications Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A.K. Suykens

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

JMLR 2020 A Statistical Learning Approach to Modal Regression Yunlong Feng, Jun Fan, Johan A.K. Suykens

AAAI 2020 Random Fourier Features via Fast Surrogate Leverage Weighted Sampling Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens

JMLR 2019 Sparse Kernel Regression with Coefficient-Based $\ell_q-$regularization Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A.K. Suykens

JMLR 2018 Kernel Density Estimation for Dynamical Systems Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A.K. Suykens

AISTATS 2018 Solving Lp-Norm Regularization with Tensor Kernels Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens

MLJ 2016 Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee Emanuele Frandi, Ricardo Ñanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens

JMLR 2015 Learning with the Maximum Correntropy Criterion Induced Losses for Regression Yunlong Feng, Xiaolin Huang, Lei Shi, Yuning Yang, Johan A.K. Suykens

MLOSS 2014 EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor

MLJ 2014 Learning with Tensors: A Framework Based on Convex Optimization and Spectral Regularization Marco Signoretto, Dinh Quoc Tran, Lieven De Lathauwer, Johan A. K. Suykens

JMLR 2014 Ramp Loss Linear Programming Support Vector Machine Xiaolin Huang, Lei Shi, Johan A.K. Suykens

JMLR 2011 Kernel Regression in the Presence of Correlated Errors Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens, Bart De Moor

JMLR 2011 Learning Transformation Models for Ranking and Survival Analysis Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel

MLJ 2011 Sparse Conjugate Directions Pursuit with Application to Fixed-Size Kernel Models Peter Karsmakers, Kristiaan Pelckmans, Kris De Brabanter, Hugo Van hamme, Johan A. K. Suykens

JMLR 2008 Model Selection in Kernel Based Regression Using the Influence Function Michiel Debruyne, Mia Hubert, Johan A.K. Suykens

MLJ 2006 Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor

MLJ 2004 Benchmarking Least Squares Support Vector Machine Classifiers Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle

NeCo 2002 Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle