| Byzantine-robust distributed learning: Towards optimal statistical rates D Yin, Y Chen, R Kannan, P Bartlett International Conference on Machine Learning, 5650-5659, 2018 | 1672 | 2018 |
| An efficient framework for clustered federated learning A Ghosh, J Chung, D Yin, K Ramchandran IEEE Transactions on Information Theory 68 (12), 8076-8091, 2022 | 983 | 2022 |
| 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 | 550 | 2019 |
| Rademacher complexity for adversarially robust generalization D Yin, R Kannan, P Bartlett International Conference on Machine Learning, 7085-7094, 2019 | 313 | 2019 |
| Robust federated learning in a heterogeneous environment A Ghosh, J Hong, D Yin, K Ramchandran arXiv preprint arXiv:1906.06629, 2019 | 275 | 2019 |
| 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 | 239 | 2019 |
| 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 | 160 | 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 | 124 | 2019 |
| 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 | 74 | 2017 |
| Architecture matters in continual learning SI Mirzadeh, A Chaudhry, D Yin, T Nguyen, R Pascanu, D Gorur, ... arXiv preprint arXiv:2202.00275, 2022 | 71 | 2022 |
| 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 | 65 | 2022 |
| Stochastic Gradient and Langevin Processes X Cheng, D Yin, P Bartlett, M Jordan arXiv preprint arXiv:1907.03215, 2019 | 55* | 2019 |
| 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 | 44* | 2020 |
| 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 | 38 | 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 | 38 | 2019 |
| 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 | 34 | 2020 |
| 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 | 34 | 2019 |
| 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 | 28* | 2022 |
| Efficient local planning with linear function approximation D Yin, B Hao, Y Abbasi-Yadkori, N Lazić, C Szepesvári International Conference on Algorithmic Learning Theory, 1165-1192, 2022 | 27 | 2022 |
| Improved regret bound and experience replay in regularized policy iteration N Lazic, D Yin, Y Abbasi-Yadkori, C Szepesvari International Conference on Machine Learning, 6032-6042, 2021 | 20 | 2021 |
