| Stochastic alternating direction method of multipliers H Ouyang, N He, L Tran, A Gray International conference on machine learning, 80-88, 2013 | 398 | 2013 |
| SBEED: Convergent reinforcement learning with nonlinear function approximation B Dai, A Shaw, L Li, L Xiao, N He, Z Liu, J Chen, L Song International Conference on Machine Learning (ICML), PMLR 80:1125-1134, 2018 | 361 | 2018 |
| Scalable kernel methods via doubly stochastic gradients B Dai, B Xie, N He, Y Liang, A Raj, MF Balcan, L Song Advances in neural information processing systems 27, 2014 | 274 | 2014 |
| Global convergence and variance-reduced optimization for a class of nonconvex-nonconcave minimax problems J Yang, N Kiyavash, N He Advances in Neural Information Processing Systems 33, 2020 | 241 | 2020 |
| Learning from conditional distributions via dual embeddings B Dai, N He, Y Pan, B Boots, L Song Artificial Intelligence and Statistics, 1458-1467, 2017 | 184* | 2017 |
| Time-sensitive recommendation from recurrent user activities N Du, Y Wang, N He, J Sun, L Song Advances in neural information processing systems 28, 2015 | 178 | 2015 |
| Stochastic Generative Hashing B Dai, R Guo, S Kumar, N He, L Song International Conference on Machine Learning, PMLR 70:913-922, 2017 | 147 | 2017 |
| Optimization for reinforcement learning: From a single agent to cooperative agents D Lee, N He, P Kamalaruban, V Cevher IEEE Signal Processing Magazine 37 (3), 123-135, 2020 | 136 | 2020 |
| Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning Y Hu, S Zhang, X Chen, N He Advances in Neural Information Processing Systems 33, 2020 | 108* | 2020 |
| Faster single-loop algorithms for minimax optimization without strong concavity J Yang, A Orvieto, A Lucchi, N He International conference on artificial intelligence and statistics, 5485-5517, 2022 | 95 | 2022 |
| The complexity of nonconvex-strongly-concave minimax optimization S Zhang, J Yang, C Guzmán, N Kiyavash, N He Uncertainty in Artificial Intelligence, 482-492, 2021 | 94 | 2021 |
| Provable Bayesian inference via particle mirror descent B Dai, N He, H Dai, L Song Artificial Intelligence and Statistics, 985-994, 2016 | 94 | 2016 |
| Robust deep learning-based diagnosis of mixed faults in rotating machinery S Chen, Y Meng, H Tang, Y Tian, N He, C Shao IEEE/ASME Transactions on Mechatronics 25 (5), 2167-2176, 2020 | 89 | 2020 |
| A catalyst framework for minimax optimization J Yang, S Zhang, N Kiyavash, N He Advances in Neural Information Processing Systems 33, 2020 | 80 | 2020 |
| On the Convergence Rate of Stochastic Mirror Descent for Nonsmooth Nonconvex Optimization S Zhang, N He arXiv preprint arXiv:1806.04781, 2018 | 79 | 2018 |
| Exponential Family Estimation via Adversarial Dynamics Embedding B Dai, Z Liu, H Dai, N He, A Gretton, L Song, D Schuurmans Advances in Neural Information Processing Systems (NeurIPS), 10977--10988, 2019 | 72 | 2019 |
| Online learning for multivariate hawkes processes Y Yang, J Etesami, N He, N Kiyavash Advances in Neural Information Processing Systems 30, 2017 | 71 | 2017 |
| Stochastic policy gradient methods: Improved sample complexity for fisher-non-degenerate policies I Fatkhullin, A Barakat, A Kireeva, N He International Conference on Machine Learning, 9827–9869, 2023 | 68 | 2023 |
| Mirror prox algorithm for multi-term composite minimization and semi-separable problems N He, A Juditsky, A Nemirovski Computational Optimization and Applications 61 (2), 275-319, 2015 | 60* | 2015 |
| Sample complexity of sample average approximation for conditional stochastic optimization Y Hu, X Chen, N He SIAM Journal on Optimization 30 (3), 2103-2133, 2020 | 57 | 2020 |
Bo DaiGoogle Brain & Georgia TechVerified email at google.com
