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Dong Yin
Dong Yin
Research Scientist, DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Byzantine-robust distributed learning: Towards optimal statistical rates
D Yin, Y Chen, R Kannan, P Bartlett
International Conference on Machine Learning, 5650-5659, 2018
6212018
A fourier perspective on model robustness in computer vision
D Yin, R Gontijo Lopes, J Shlens, ED Cubuk, J Gilmer
Advances in Neural Information Processing Systems 32, 13276-13286, 2019
2832019
An efficient framework for clustered federated learning
A Ghosh, J Chung, D Yin, K Ramchandran
Advances in Neural Information Processing Systems 33, 19586-19597, 2020
2352020
Rademacher complexity for adversarially robust generalization
D Yin, R Kannan, P Bartlett
International Conference on Machine Learning, 7085-7094, 2019
1962019
Improving robustness without sacrificing accuracy with patch gaussian augmentation
RG Lopes, D Yin, B Poole, J Gilmer, ED Cubuk
arXiv preprint arXiv:1906.02611, 2019
1372019
Robust federated learning in a heterogeneous environment
A Ghosh, J Hong, D Yin, K Ramchandran
arXiv preprint arXiv:1906.06629, 2019
1182019
Gradient diversity: a key ingredient for scalable distributed learning
D Yin, A Pananjady, M Lam, D Papailiopoulos, K Ramchandran, P Bartlett
International Conference on Artificial Intelligence and Statistics, 1998-2007, 2018
115*2018
Defending against saddle point attack in Byzantine-robust distributed learning
D Yin, Y Chen, R Kannan, P Bartlett
International Conference on Machine Learning, 7074-7084, 2019
792019
Phasecode: Fast and efficient compressive phase retrieval based on sparse-graph codes
R Pedarsani, D Yin, K Lee, K Ramchandran
IEEE Transactions on Information Theory 63 (6), 3663-3691, 2017
652017
Learning mixtures of sparse linear regressions using sparse graph codes
D Yin, R Pedarsani, Y Chen, K Ramchandran
IEEE Transactions on Information Theory 65 (3), 1430-1451, 2019
302019
Stochastic Gradient and Langevin Processes
X Cheng, D Yin, P Bartlett, M Jordan
arXiv preprint arXiv:1907.03215, 2019
27*2019
Sub-linear time support recovery for compressed sensing using sparse-graph codes
X Li, D Yin, S Pawar, R Pedarsani, K Ramchandran
IEEE Transactions on Information Theory 65 (10), 6580-6619, 2019
272019
Sub-linear time support recovery for compressed sensing using sparse-graph codes
X Li, D Yin, S Pawar, R Pedarsani, K Ramchandran
IEEE Transactions on Information Theory 65 (10), 6580-6619, 2019
272019
Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint
D Yin, M Farajtabar, A Li, N Levine, A Mott
arXiv preprint arXiv:2006.10974, 2020
24*2020
Distributed sequence memory of multidimensional inputs in recurrent networks
AS Charles, D Yin, CJ Rozell
The Journal of Machine Learning Research 18 (1), 181-217, 2017
152017
Fast and robust compressive phase retrieval with sparse-graph codes
D Yin, K Lee, R Pedarsani, K Ramchandran
2015 IEEE International Symposium on Information Theory (ISIT), 2583-2587, 2015
132015
Architecture matters in continual learning
SI Mirzadeh, A Chaudhry, D Yin, T Nguyen, R Pascanu, D Gorur, ...
arXiv preprint arXiv:2202.00275, 2022
112022
The Effectiveness of Memory Replay in Large Scale Continual Learning
Y Balaji, M Farajtabar, D Yin, A Mott, A Li
arXiv preprint arXiv:2010.02418, 2020
112020
Wide neural networks forget less catastrophically
SI Mirzadeh, A Chaudhry, D Yin, H Hu, R Pascanu, D Gorur, M Farajtabar
International Conference on Machine Learning, 15699-15717, 2022
102022
An instance-dependent simulation framework for learning with label noise
K Gu, X Masotto, V Bachani, B Lakshminarayanan, J Nikodem, D Yin
Machine Learning, 1-26, 2022
8*2022
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Articles 1–20