Obserwuj
Jerry Li
Jerry Li
Microsoft Research
Zweryfikowany adres z mit.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
1125*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
3562019
Spectral signatures in backdoor attacks
B Tran, J Li, A Madry
Advances in neural information processing systems 31, 2018
3272018
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
3102019
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
2102019
Byzantine stochastic gradient descent
D Alistarh, Z Allen-Zhu, J Li
Advances in Neural Information Processing Systems 31, 2018
2002018
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
1912017
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
1252015
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
1192018
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
1102018
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
902020
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
802017
Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
672019
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
662019
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
492020
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
472015
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
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20