Johan Suykens
12 papers · 2007–2025 · 4 venues · across top CS/AI venues
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Machine Learning
(12)
Optimization & Theory · Machine Learning
(10)
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(7)
Mathematics & Optimization
(6)
Optimization
(5)
Deep Learning
(4)
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(2)
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(1)
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(1)
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(1)
Optimization & Theory · Deep Learning
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kernel methods
(4)
kernel ridge regression
(2)
double descent
(2)
kernel principal component analysis
(2)
random feature
(2)
singular value decomposition
(2)
model architecture
(1)
nonparametric learning
(1)
stochastic gradient descent
(1)
low-rank approximation
(1)
kernel approximation
(1)
risk minimization
(1)
kernel pca
(1)
reproducing kernel hilbert space
(1)
spectral clustering
(1)
rademacher complexity
(1)
computational efficiency
(1)
margin maximization
(1)
high-dimensional regression
(1)
gradient descent
(1)
Papers
Accelerating Spectral Clustering under Fairness Constraints
ICML 2025
Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method
ICML 2024
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
ICML 2024
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
ICML 2023
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
NeurIPS 2023
On the Double Descent of Random Features Models Trained with SGD
NeurIPS 2022
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures
AISTATS 2021
Kernel regression in high dimensions: Refined analysis beyond double descent
AISTATS 2021
A Theoretical Framework for Target Propagation
NeurIPS 2020
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
AAAI 2020
Solving lp-norm regularization with tensor kernels
AISTATS 2018
A Risk Minimization Principle for a Class of Parzen Estimators
NeurIPS 2007