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, UCLVerified email at gatsby.ucl.ac.uk
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Bharath SriperumbudurPennsylvania State UniversityVerified email at psu.edu
Laszlo A. JeniSystems Faculty in the Robotics Institute, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Dino SejdinovicAssociate Professor, University of OxfordVerified email at stats.ox.ac.uk
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
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 LivingstoneLecturer in Mathematical Statistics, University College LondonVerified email at ucl.ac.uk
Heiko StrathmannResearch Scientist at DeepmindVerified email at deepmind.com
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
Balaji LakshminarayananStaff Research Scientist at Google BrainVerified email at google.com

Zoltan Szabo

Professor, LSE

Verified email at lse.ac.uk - Homepage

information theory kernel methods statistical machine learning


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Information theoretical estimators toolbox Z Szabó Journal of Machine Learning Research 15 (1), 283-287, 2014	114	2014
Learning Theory for Distribution Regression Z Szabo, B Sriperumbudur, B Poczos, A Gretton Journal of Machine Learning Research 17 (152), 1-40, 2016	109	2016
Optimal Rates for Random Fourier Features B Sriperumbudur, Z Szabo Advances in Neural Information Processing Systems (NIPS), 1144-1152, 2015	107	2015
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	89	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	84	2015
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	78	2016
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	78	2015
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
Online group-structured dictionary learning Z Szabo, B Poczos, A Lorincz IEEE Computer Vision and Pattern Recognition (CVPR), 2865-2872, 2011	71	2011
Undercomplete blind subspace deconvolution Z Szabó, B Póczos, A Lőrincz Journal of Machine Learning Research 8, 1063-1095, 2007	66	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	46	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	43	2014
Characteristic and Universal Tensor Product Kernels Z Szabo, B Sriperumbudur Journal of Machine Learning Research 18 (233), 1-29, 2018	39	2018
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	37	2017
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	35	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	22	2019
Independent process analysis without a priori dimensional information B Póczos, Z Szabó, M Kiszlinger, A Lörincz Proceedings of the 7th international conference on Independent component …, 2007	21	2007

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