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 Google DeepmindVerified email at gatsby.ucl.ac.uk
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Laszlo A. JeniAssistant Professor 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, TU WienVerified email at tuwien.ac.at
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, Foundations of Machine Learning Systems group, Tuebingen AI CenterVerified email at uni-tuebingen.de
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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Learning Theory for Distribution Regression Z Szabo, B Sriperumbudur, B Poczos, A Gretton Journal of Machine Learning Research 17 (152), 1-40, 2016	180	2016
Optimal Rates for Random Fourier Features B Sriperumbudur, Z Szabo Advances in Neural Information Processing Systems (NIPS), 1144-1152, 2015	161	2015
Information theoretical estimators toolbox Z Szabó Journal of Machine Learning Research 15 (1), 283-287, 2014	146	2014
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	132	2016
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), 261-270, 2017	122	2017
Two-stage Sampled Learning Theory on Distributions Z Szabó, A Gretton, B Póczos, B Sriperumbudur International Conference on Artificial Intelligence and Statistics (AISTATS …, 2015	104	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	94	2015
Characteristic and Universal Tensor Product Kernels Z Szabo, B Sriperumbudur Journal of Machine Learning Research 18 (233), 1-29, 2018	80	2018
Online group-structured dictionary learning Z Szabo, B Poczos, A Lorincz IEEE Computer Vision and Pattern Recognition (CVPR), 2865-2872, 2011	78	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	64	2007
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
Separation theorem for independent subspace analysis and its consequences Z Szabó, B Póczos, A Lõrincz Pattern Recognition 45 (4), 1782-1791, 2012	53	2012
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	52	2017
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	43	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), 3782-3793, 2019	36	2019
Cross-entropy optimization for independent process analysis Z Szabó, B Póczos, A Lőrincz Independent Component Analysis and Blind Signal Separation (ICA), 909-916, 2006	36	2006
Hard Shape-Constrained Kernel Machines PC Aubin-Frankowski, Z Szabo Advances in Neural Information Processing Systems (NeurIPS), 384-395, 2020	29	2020
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	29*	2015

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