Jerry Li
Jerry Li
Microsoft Research AI
Email verificata su mit.edu - Home page
Titolo
Citata da
Citata da
Anno
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
493*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
2182019
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
125*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
1102019
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
1072017
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
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
1002019
Spectral signatures in backdoor attacks
B Tran, J Li, A Madry
Advances in Neural Information Processing Systems, 8000-8010, 2018
1002018
Byzantine stochastic gradient descent
D Alistarh, Z Allen-Zhu, J Li
Advances in Neural Information Processing Systems, 4613-4623, 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
80*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
772018
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
662018
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
57*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
562017
Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
352019
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
342015
Robust and proper learning for mixtures of gaussians via systems of polynomial inequalities
J Li, L Schmidt
Conference on Learning Theory, 1302-1382, 2017
31*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 30, 6391-6401, 2017
272017
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
192018
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20