Jerry Li
Jerry Li
Microsoft Research
Verified email at mit.edu - Homepage
Title
Cited by
Cited by
Year
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
640*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
2522019
Provably robust deep learning via adversarially trained smoothed classifiers
H Salman, G Yang, J Li, P Zhang, H Zhang, I Razenshteyn, S Bubeck
arXiv preprint arXiv:1906.04584, 2019
1522019
Spectral signatures in backdoor attacks
B Tran, J Li, A Madry
arXiv preprint arXiv:1811.00636, 2018
1452018
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
145*2017
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
1312017
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
1242019
Byzantine stochastic gradient descent
D Alistarh, Z Allen-Zhu, J Li
arXiv preprint arXiv:1803.08917, 2018
1172018
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
1052015
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
892018
Computationally efficient robust sparse estimation in high dimensions
S Balakrishnan, SS Du, J Li, A Singh
Conference on Learning Theory, 169-212, 2017
88*2017
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
772018
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
652017
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
64*2018
Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
392019
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
362015
Robust and proper learning for mixtures of gaussians via systems of polynomial inequalities
J Li, L Schmidt
Conference on Learning Theory, 1302-1382, 2017
35*2017
Quantum entropy scoring for fast robust mean estimation and improved outlier detection
Y Dong, SB Hopkins, J Li
arXiv preprint arXiv:1906.11366, 2019
342019
Communication-efficient distributed learning of discrete distributions
I Diakonikolas, E Grigorescu, J Li, A Natarajan, K Onak, L Schmidt
NIPS, 2017
312017
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
282020
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Articles 1–20