Zoltán Szabó

Affiliations:
  • École Polytechnique, Department of Applied Mathematics, Palaiseau, France
  • University College London, Gatsby Computational Neuroscience Unit, UK
  • Eötvös Loránd University, Budapest, Hungary


According to our database1, Zoltán Szabó authored at least 45 papers between 2004 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2022
Handling Hard Affine SDP Shape Constraints in RKHSs.
J. Mach. Learn. Res., 2022

Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'.
CoRR, 2022

2020
Hard Shape-Constrained Kernel Machines.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
On Kernel Derivative Approximation with Random Fourier Features.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2017
Characteristic and Universal Tensor Product Kernels.
J. Mach. Learn. Res., 2017

A Linear-Time Kernel Goodness-of-Fit Test.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

An Adaptive Test of Independence with Analytic Kernel Embeddings.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Learning Theory for Distribution Regression.
J. Mach. Learn. Res., 2016

Interpretable Distribution Features with Maximum Testing Power.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Optimal Rates for Random Fourier Features.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM).
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Two-stage sampled learning theory on distributions.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Information theoretical estimators toolbox.
J. Mach. Learn. Res., 2014

Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding.
CoRR, 2014

Spatio-temporal Event Classification Using Time-Series Kernel Based Structured Sparsity.
Proceedings of the Computer Vision - ECCV 2014, 2014

2013
Explaining Unintelligible Words by Means of their Context.
Proceedings of the ICPRAM 2013, 2013

Wikifying Novel Words to Mixtures of Wikipedia Senses by Structured Sparse Coding.
Proceedings of the Pattern Recognition Applications and Methods - International Conference, 2013

Emotional Expression Classification Using Time-Series Kernels.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012
Separation theorem for independent subspace analysis and its consequences.
Pattern Recognit., 2012

3D shape estimation in video sequences provides high precision evaluation of facial expressions.
Image Vis. Comput., 2012

Distributed high dimensional information theoretical image registration via random projections.
Digit. Signal Process., 2012

Automated Word Puzzle Generation via Topic Dictionaries
CoRR, 2012

Collaborative Filtering via Group-Structured Dictionary Learning.
Proceedings of the Latent Variable Analysis and Signal Separation, 2012

2011
Nonparametric independent process analysis.
Proceedings of the 19th European Signal Processing Conference, 2011

Nonparametric divergence estimators for independent subspace analysis.
Proceedings of the 19th European Signal Processing Conference, 2011

Online group-structured dictionary learning.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010
Auto-regressive independent process analysis without combinatorial efforts.
Pattern Anal. Appl., 2010

Towards Nonstationary, Nonparametric Independent Process Analysis with Unknown Source Component Dimensions
CoRR, 2010

Autoregressive independent process analysis with missing observations.
Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

2009
Separation Principles in Independent Process Analysis
PhD thesis, 2009

Complex Independent Process Analysis.
Acta Cybern., 2009

Controlled Complete ARMA Independent Process Analysis.
Proceedings of the International Joint Conference on Neural Networks, 2009

Fast Parallel Estimation of High Dimensional Information Theoretical Quantities with Low Dimensional Random Projection Ensembles.
Proceedings of the Independent Component Analysis and Signal Separation, 2009

Complete Blind Subspace Deconvolution.
Proceedings of the Independent Component Analysis and Signal Separation, 2009

2007
Undercomplete Blind Subspace Deconvolution.
J. Mach. Learn. Res., 2007

Neurally plausible, non-combinatorial iterative independent process analysis.
Neurocomputing, 2007

Independent Subspace Analysis can Cope with the 'Curse of Dimensionality'.
Acta Cybern., 2007

Post Nonlinear Independent Subspace Analysis.
Proceedings of the Artificial Neural Networks, 2007

Independent Process Analysis Without a Priori Dimensional Information.
Proceedings of the Independent Component Analysis and Signal Separation, 2007

Undercomplete Blind Subspace Deconvolution Via Linear Prediction.
Proceedings of the Machine Learning: ECML 2007, 2007

2006
ε-Sparse Representations: Generalized Sparse Approximation and the Equivalent Family of SVM Tasks.
Acta Cybern., 2006

Cross-Entropy Optimization for Independent Process Analysis.
Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

2004
L1 regularization is better than L2 for learning and predicting chaotic systems
CoRR, 2004


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