Jerry Li
Jerry Li
Microsoft Research
Verifierad e-postadress på mit.edu - Startsida
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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
2302019
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
1212019
Spectral signatures in backdoor attacks
B Tran, J Li, A Madry
Advances in Neural Information Processing Systems, 8000-8010, 2018
1172018
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
1112017
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
1042019
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
1032015
Byzantine stochastic gradient descent
D Alistarh, Z Allen-Zhu, J Li
Advances in Neural Information Processing Systems 31, 4613-4623, 2018
1022018
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
792018
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
692018
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
582017
Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
362019
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
352015
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
292017
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
232013
Principled approaches to robust machine learning and beyond
JZ Li
Massachusetts Institute of Technology, 2018
202018
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Artiklar 1–20