| Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images P Khosravi, E Kazemi, M Imielinski, O Elemento, I Hajirasouliha EBioMedicine 27 (317-328), 2017 | 336 | 2017 |
| Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images P Khosravi, E Kazemi, M Imielinski, O Elemento, I Hajirasouliha bioRxiv, 19, 2017 | 336 | 2017 |
| Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization P Khosravi, E Kazemi, Q Zhan, JE Malmsten, M Toschi, P Zisimopoulos, ... NPJ digital medicine 2 (1), 21, 2019 | 320 | 2019 |
| Hiding in the Mobile Crowd: LocationPrivacy through Collaboration R Shokri, G Theodorakopoulos, P Papadimitratos, E Kazemi, JP Hubaux Dependable and Secure Computing, IEEE Transactions on 11 (3), 266-279, 2014 | 233 | 2014 |
| Growing a Graph Matching from a Handful of Seeds E Kazemi, SH Hassani, M Grossglauser Proceedings of the Vldb Endowment International Conference on Very Large …, 2015 | 135 | 2015 |
| Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity E Kazemi, M Mitrovic, M Zadimoghaddam, S Lattanzi, A Karbasi International Conference on Machine Learning, 3311--3320, 2019 | 103 | 2019 |
| Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints E Kazemi, M Zadimoghaddam, A Karbasi International Conference on Machine Learning, 2549-2558, 2018 | 88* | 2018 |
| Been there, done that: What your mobility traces reveal about your behavior V Etter, M Kafsi, E Kazemi Mobile data challenge by nokia workshop, in conjunction with int. conf. on …, 2012 | 84 | 2012 |
| Do less, get more: Streaming submodular maximization with subsampling M Feldman, A Karbasi, E Kazemi Advances in Neural Information Processing Systems, 730-740, 2018 | 77 | 2018 |
| Where to go from here? Mobility prediction from instantaneous information V Etter, M Kafsi, E Kazemi, M Grossglauser, P Thiran Pervasive and Mobile Computing 9 (6), 784-797, 2013 | 76 | 2013 |
| A deep learning approach to diagnostic classification of prostate cancer using pathology–radiology fusion P Khosravi, M Lysandrou, M Eljalby, Q Li, E Kazemi, P Zisimopoulos, ... Journal of Magnetic Resonance Imaging 54 (2), 462-471, 2021 | 74 | 2021 |
| On Adversarial Bias and the Robustness of Fair Machine Learning H Chang, TD Nguyen, SK Murakonda, E Kazemi, R Shokri arXiv preprint arXiv:2006.08669, 2020 | 57 | 2020 |
| Data Summarization at Scale: A Two-Stage Submodular Approach M Mitrovic, E Kazemi, M Zadimoghaddam, A Karbasi International Conference on Machine Learning, 3593-3602, 2018 | 53 | 2018 |
| PROPER: global protein interaction network alignment through percolation matching E Kazemi, H Hassani, M Grossglauser, H Pezeshgi Modarres BMC bioinformatics 17, 1-16, 2016 | 53 | 2016 |
| When Can Two Unlabeled Networks Be Aligned Under Partial Overlap? E Kazemi, L Yartseva, M Grossglauser Proceedings of the 53rd Annual Allerton Conference on Communication, Control …, 2015 | 46 | 2015 |
| Regularized submodular maximization at scale E Kazemi, S Minaee, M Feldman, A Karbasi International Conference on Machine Learning, 5356-5366, 2021 | 42 | 2021 |
| Streaming submodular maximization under a k-set system constraint R Haba, E Kazemi, M Feldman, A Karbasi International Conference on Machine Learning, 3939-3949, 2020 | 38 | 2020 |
| Adaptive sequence submodularity M Mitrovic, E Kazemi, M Feldman, A Krause, A Karbasi Advances in Neural Information Processing Systems, 5352-5363, 2019 | 30 | 2019 |
| Robust Automated Assessment of Human Blastocyst Quality using Deep Learning P Khosravi, E Kazemi, Q Zhan, M Toschi, JE Malmsten, C Hickman, ... bioRxiv, 394882, 2018 | 21 | 2018 |
| Mitigating epidemics through mobile micro-measures M Kafsi, E Kazemi, L Maystre, L Yartseva, M Grossglauser, P Thiran Third International Conference on the Analysis of Mobile Phone Datasets, 2013 | 19 | 2013 |
Amin KarbasiRobust Intelligence, Yale University (on leave)在 yale.edu 的电子邮件经过验证
