| Learning Theory for Distribution Regression Z Szabo, B Sriperumbudur, B Poczos, A Gretton Journal of Machine Learning Research 17 (152), 1-40, 2016 | 177 | 2016 |
| Optimal Rates for Random Fourier Features B Sriperumbudur, Z Szabo Advances in Neural Information Processing Systems (NIPS), 1144-1152, 2015 | 157 | 2015 |
| Information theoretical estimators toolbox Z Szabó Journal of Machine Learning Research 15 (1), 283-287, 2014 | 142 | 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 | 129 | 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 | 118 | 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 | 103 | 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 | 93 | 2015 |
| Characteristic and Universal Tensor Product Kernels Z Szabo, B Sriperumbudur Journal of Machine Learning Research 18 (233), 1-29, 2018 | 78 | 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 | 42 | 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 | 36 | 2006 |
| 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 | 35 | 2019 |
| 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 | 28* | 2015 |
Lorincz, AndrasEotvos Lorand University在 inf.elte.hu 的电子邮件经过验证
