| Apparatus and Method for Analyzing Driving Performance Data A Freiberger, D Izhaky, A Painsky, A Shamir, Z Bendet, O Steinberg, ... US Patent App. 13/972,134, 2014 | 307 | 2014 |
| Apparatus and Method for Detecting Driving Performance Data O Steinberg, A Tamir, A Freiberger, D Izhaky, A Painsky, D Aviv, P Bachar US Patent App. 13/964,568, 2014 | 179 | 2014 |
| Cross-validated variable selection in tree-based methods improves predictive performance A Painsky, S Rosset IEEE transactions on pattern analysis and machine intelligence 39 (11), 2142 …, 2016 | 86 | 2016 |
| Feature importance in gradient boosting trees with cross-validation feature selection AI Adler, A Painsky Entropy 24 (5), 687, 2022 | 82 | 2022 |
| On the universality of the logistic loss function A Painsky, G Wornell 2018 IEEE International Symposium on Information Theory (ISIT), 936-940, 2018 | 51 | 2018 |
| Bregman divergence bounds and universality properties of the logarithmic loss A Painsky, GW Wornell IEEE Transactions on Information Theory 66 (3), 1658-1673, 2019 | 37* | 2019 |
| Generalized independent component analysis over finite alphabets A Painsky, S Rosset, M Feder IEEE Transactions on Information Theory 62 (2), 1038-1053, 2016 | 35 | 2016 |
| Safety of the fourth COVID-19 BNT162b2 mRNA (second booster) vaccine: a prospective and retrospective cohort study M Yechezkel, M Mofaz, A Painsky, T Patalon, S Gazit, E Shmueli, D Yamin The Lancet Respiratory Medicine 11 (2), 139-150, 2023 | 30 | 2023 |
| Low rank approximation of binary matrices: Column subset selection and generalizations C Dan, KA Hansen, H Jiang, L Wang, Y Zhou arXiv preprint arXiv:1511.01699, 2015 | 29 | 2015 |
| Gaussian lower bound for the information bottleneck limit A Painsky, N Tishby Journal of Machine Learning Research 18 (213), 1-29, 2018 | 19 | 2018 |
| Linear independent component analysis over finite fields: Algorithms and bounds A Painsky, S Rosset, M Feder IEEE Transactions on Signal Processing 66 (22), 5875-5886, 2018 | 18 | 2018 |
| Representation compression and generalization in deep neural networks R Shwartz-Ziv, A Painsky, N Tishby | 18 | 2018 |
| On extending neural networks with loss ensembles for text classification H Hajiabadi, D Molla-Aliod, R Monsefi arXiv preprint arXiv:1711.05170, 2017 | 17 | 2017 |
| Generalized binary independent component analysis A Painsky, S Rosset, M Feder Information Theory (ISIT), 2014 IEEE International Symposium on, 1326-1330, 2014 | 16 | 2014 |
| Lossless compression of random forests A Painsky, S Rosset Journal of Computer Science and Technology 34, 494-506, 2019 | 15 | 2019 |
| Optimal and maximin procedures for multiple testing problems S Rosset, R Heller, A Painsky, E Aharoni Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022 | 14* | 2022 |
| Nonlinear canonical correlation analysis: A compressed representation approach A Painsky, M Feder, N Tishby Entropy 22 (2), 208, 2020 | 14* | 2020 |
| Data fusion in high computational load environments A Painsky US Patent 8,805,648, 2014 | 14 | 2014 |
| Memoryless representation of Markov processes A Painsky, S Rosset, M Feder 2013 IEEE International Symposium on Information Theory, 2294-298, 2013 | 14 | 2013 |
| Large alphabet source coding using independent component analysis A Painsky, S Rosset, M Feder IEEE Transactions on Information Theory 63 (10), 6514-6529, 2017 | 13 | 2017 |
Saharon RossetTel Aviv UniversityVerifisert e-postadresse på post.tau.ac.il