| QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic Advances in Neural Information Processing Systems, 1707-1718, 2017 | 531* | 2017 |
| Robust estimators in high-dimensions without the computational intractability I Diakonikolas, G Kamath, D Kane, J Li, A Moitra, A Stewart SIAM Journal on Computing 48 (2), 742-864, 2019 | 230 | 2019 |
| ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang International Conference on Machine Learning, 4035-4043, 2017 | 133* | 2017 |
| Provably robust deep learning via adversarially trained smoothed classifiers H Salman, J Li, I Razenshteyn, P Zhang, H Zhang, S Bubeck, G Yang Advances in Neural Information Processing Systems, 11292-11303, 2019 | 121 | 2019 |
| Spectral signatures in backdoor attacks B Tran, J Li, A Madry Advances in Neural Information Processing Systems, 8000-8010, 2018 | 117 | 2018 |
| Being robust (in high dimensions) can be practical I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart arXiv preprint arXiv:1703.00893, 2017 | 111 | 2017 |
| Sever: A robust meta-algorithm for stochastic optimization I Diakonikolas, G Kamath, D Kane, J Li, J Steinhardt, A Stewart International Conference on Machine Learning, 1596-1606, 2019 | 104 | 2019 |
| The spraylist: A scalable relaxed priority queue D Alistarh, J Kopinsky, J Li, N Shavit Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of …, 2015 | 103 | 2015 |
| Byzantine stochastic gradient descent D Alistarh, Z Allen-Zhu, J Li Advances in Neural Information Processing Systems 31, 4613-4623, 2018 | 102 | 2018 |
| Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017 | 82* | 2017 |
| Robustly learning a gaussian: Getting optimal error, efficiently I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 79 | 2018 |
| Mixture models, robustness, and sum of squares proofs SB Hopkins, J Li Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing …, 2018 | 69 | 2018 |
| On the limitations of first-order approximation in gan dynamics J Li, A Madry, J Peebles, L Schmidt International Conference on Machine Learning, 3005-3013, 2018 | 62* | 2018 |
| Sample-optimal density estimation in nearly-linear time J Acharya, I Diakonikolas, J Li, L Schmidt Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 58 | 2017 |
| Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019 | 36 | 2019 |
| Fast and near-optimal algorithms for approximating distributions by histograms J Acharya, I Diakonikolas, C Hegde, JZ Li, L Schmidt Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2015 | 35 | 2015 |
| Robust and proper learning for mixtures of gaussians via systems of polynomial inequalities J Li, L Schmidt Conference on Learning Theory, 1302-1382, 2017 | 33* | 2017 |
| Communication-efficient distributed learning of discrete distributions I Diakonikolas, E Grigorescu, J Li, A Natarajan, K Onak, L Schmidt Advances in Neural Information Processing Systems, 6391-6401, 2017 | 29 | 2017 |
| Lower bounds for exact model counting and applications in probabilistic databases P Beame, J Li, S Roy, D Suciu arXiv preprint arXiv:1309.6815, 2013 | 23 | 2013 |
| Principled approaches to robust machine learning and beyond JZ Li Massachusetts Institute of Technology, 2018 | 20 | 2018 |
Dan AlistarhIST AustriaVerifierad e-postadress på csail.mit.edu
