Zoltan Szabo

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Lorincz, AndrasEotvos Lorand UniversityVerified email at inf.elte.hu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Arthur GrettonGatsby Computational Neuroscience Unit and DeepmindVerified email at gatsby.ucl.ac.uk
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Dino SejdinovicProfessor, University of AdelaideVerified email at adelaide.edu.au
Laszlo A. JeniSystems Faculty in the Robotics Institute, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Gyula VörösEötvös Loránd TudományegyetemVerified email at inf.elte.hu
Balázs PintérEötvös Loránd UniversityVerified email at elte.hu
Jeffrey CohnProfessor of Psychology, Psychiatry, and Intelligent Systems,University of PittsburghVerified email at pitt.edu
Takeo KanadeCarnegie Mellon UniversityVerified email at cs.cmu.edu
Zsolt PalotaiSenior Researcher, Eötvös Loránd UniversityVerified email at caesar.elte.hu
Pierre-Cyril Aubin-FrankowskiPhD graduate, INRIA ParisVerified email at inria.fr
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisVerified email at telecom-paris.fr
Alex LambertPostdoctoral fellow, KU LeuvenVerified email at kuleuven.be
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Wenkai XuPostdoc, Department of Statistics, University of OxfordVerified email at stats.ox.ac.uk
S. M. Ali EslamiGoogle DeepMindVerified email at google.com
Samuel LivingstoneAssociate professor in mathematical statistics, University College LondonVerified email at ucl.ac.uk
Heiko StrathmannResearch Scientist at DeepmindVerified email at deepmind.com

Zoltan Szabo

Professor, LSE

Verified email at lse.ac.uk - Homepage

kernel methods information theory statistical machine learning


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Learning Theory for Distribution Regression Z Szabo, B Sriperumbudur, B Poczos, A Gretton Journal of Machine Learning Research 17 (152), 1-40, 2016	131	2016
Optimal Rates for Random Fourier Features B Sriperumbudur, Z Szabo Advances in Neural Information Processing Systems (NIPS), 1144-1152, 2015	131	2015
Information theoretical estimators toolbox Z Szabó Journal of Machine Learning Research 15 (1), 283-287, 2014	130	2014
A Linear-Time Kernel Goodness-of-Fit Test W Jitkrittum, W Xu, Z Szabo, K Fukumizu, A Gretton Advances in Neural Information Processing Systems (NIPS), 2017	106	2017
Interpretable Distribution Features with Maximum Testing Power W Jitkrittum, Z Szabo, K Chwialkowski, A Gretton Advances in Neural Information Processing Systems (NIPS), 181-189, 2016	105	2016
Two-stage Sampled Learning Theory on Distributions Z Szabó, A Gretton, B Póczos, B Sriperumbudur International Conference on Artificial Intelligence and Statistics (AISTATS …, 2015	89	2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families H Strathmann, D Sejdinovic, S Livingston, Z Szabo, A Gretton Advances in Neural Information Processing Systems (NIPS), 955-963, 2015	85	2015
Online group-structured dictionary learning Z Szabo, B Poczos, A Lorincz IEEE Computer Vision and Pattern Recognition (CVPR), 2865-2872, 2011	77	2011
3D shape estimation in video sequences provides high precision evaluation of facial expressions L Jeni, A Lorincz, T Nagy, Z Palotai, J Sebok, Z Szabo, D Takacs Image and Vision Computing, 2012	72	2012
Undercomplete blind subspace deconvolution Z Szabó, B Póczos, A Lőrincz Journal of Machine Learning Research 8, 1063-1095, 2007	65	2007
Characteristic and Universal Tensor Product Kernels Z Szabo, B Sriperumbudur Journal of Machine Learning Research 18 (233), 1-29, 2018	60	2018
Emotional expression classification using time-series kernels A Lorincz, L Jeni, Z Szabo, J Cohn, T Kanade IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW …, 2013	60	2013
An Adaptive Test of Independence with Analytic Kernel Embeddings W Jitkrittum, Z Szabo, A Gretton International Conference on Machine Learning (ICML) 70, 1742-1751, 2017	50	2017
Separation theorem for independent subspace analysis and its consequences Z Szabó, B Póczos, A Lõrincz Pattern Recognition 45 (4), 1782-1791, 2012	48	2012
Spatio-temporal event classification using time-series kernel based structured sparsity L Jeni, A Lőrincz, Z Szabó, J Cohn, T Kanade European Conference on Computer Vision (ECCV) 8692, 135-150, 2014	46	2014
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages W Jitkrittum, A Gretton, N Heess, A Eslami, B Lakshminarayanan, ... Conference on Uncertainty in Artificial Intelligence (UAI), 405-414, 2015	39	2015
Cross-entropy optimization for independent process analysis Z Szabó, B Póczos, A Lőrincz Independent Component Analysis and Blind Signal Separation (ICA), 909-916, 2006	35	2006
Bayesian Manifold Learning: The Locally Linear Latent Variable Model M Park, W Jitkrittum, A Qamar, Z Szabó, L Buesing, M Sahani Advances in Neural Information Processing Systems (NIPS), 154-162, 2015	27*	2015
MONK – Outlier-Robust Mean Embedding Estimation by Median-of-Means M Lerasle, Z Szabo, T Mathieu, G Lecue International Conference on Machine Learning (ICML), 2019	26	2019
Hard Shape-Constrained Kernel Machines PC Aubin-Frankowski, Z Szabo Advances in Neural Information Processing Systems (NeurIPS), 2020	24	2020

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