| 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 | 1118* | 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 | 355 | 2019 |
| Spectral signatures in backdoor attacks B Tran, J Li, A Madry Advances in neural information processing systems 31, 2018 | 324 | 2018 |
| 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 32, 2019 | 308 | 2019 |
| 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 | 210 | 2019 |
| Byzantine stochastic gradient descent D Alistarh, Z Allen-Zhu, J Li Advances in Neural Information Processing Systems 31, 2018 | 199 | 2018 |
| Being robust (in high dimensions) can be practical I Diakonikolas, G Kamath, DM Kane, J Li, A Moitra, A Stewart International Conference on Machine Learning, 999-1008, 2017 | 190 | 2017 |
| 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 | 183* | 2017 |
| 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 | 125 | 2015 |
| 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 | 119 | 2018 |
| Computationally efficient robust sparse estimation in high dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on learning theory, 169-212, 2017 | 111* | 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 | 110 | 2018 |
| Randomized smoothing of all shapes and sizes G Yang, T Duan, JE Hu, H Salman, I Razenshteyn, J Li International Conference on Machine Learning, 10693-10705, 2020 | 90 | 2020 |
| 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 | 82* | 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 | 80 | 2017 |
| Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019 | 67 | 2019 |
| Quantum entropy scoring for fast robust mean estimation and improved outlier detection Y Dong, S Hopkins, J Li Advances in Neural Information Processing Systems 32, 2019 | 65 | 2019 |
| Aligning ai with shared human values D Hendrycks, C Burns, S Basart, A Critch, J Li, D Song, J Steinhardt arXiv preprint arXiv:2008.02275, 2020 | 48 | 2020 |
| 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 | 47 | 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 | 44* | 2017 |
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