| Reshaped Wirtinger Flow and Incremental Algorithms for Solving Quadratic Systems of Equations H Zhang, Y Zhou, Y Liang, Y Chi arXiv preprint, 0 | 168* | |
| Spiderboost and momentum: Faster variance reduction algorithms Z Wang, K Ji, Y Zhou, Y Liang, V Tarokh Advances in Neural Information Processing Systems 32, 2019 | 131 | 2019 |
| A nonconvex approach for phase retrieval: Reshaped wirtinger flow and incremental algorithms H Zhang, Y Zhou, Y Liang, Y Chi Journal of Machine Learning Research 18, 2017 | 117 | 2017 |
| Spiderboost: A class of faster variance-reduced algorithms for nonconvex optimization Z Wang, K Ji, Y Zhou, Y Liang, V Tarokh arXiv 2018, 2018 | 84 | 2018 |
| Convergence analysis of proximal gradient with momentum for nonconvex optimization Q Li, Y Zhou, Y Liang, PK Varshney International Conference on Machine Learning, 2111-2119, 2017 | 81 | 2017 |
| Toward understanding the impact of staleness in distributed machine learning W Dai, Y Zhou, N Dong, H Zhang, EP Xing arXiv preprint arXiv:1810.03264, 2018 | 60 | 2018 |
| SGD Converges to Global Minimum in Deep Learning via Star-convex Path Y Zhou, J Yang, H Zhang, Y Liang, V Tarokh ICLR2019, 2018 | 59 | 2018 |
| Critical points of linear neural networks: Analytical forms and landscape properties Y Zhou, Y Liang ICLR2018, 2018 | 56 | 2018 |
| Improved zeroth-order variance reduced algorithms and analysis for nonconvex optimization K Ji, Z Wang, Y Zhou, Y Liang International conference on machine learning, 3100-3109, 2019 | 52 | 2019 |
| Stochastic variance-reduced cubic regularization for nonconvex optimization Z Wang, Y Zhou, Y Liang, G Lan The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 47 | 2019 |
| Reanalysis of variance reduced temporal difference learning T Xu, Z Wang, Y Zhou, Y Liang arXiv preprint arXiv:2001.01898, 2020 | 44 | 2020 |
| Fedcluster: Boosting the convergence of federated learning via cluster-cycling C Chen, Z Chen, Y Zhou, B Kailkhura 2020 IEEE International Conference on Big Data (Big Data), 5017-5026, 2020 | 35 | 2020 |
| On convergence of model parallel proximal gradient algorithm for stale synchronous parallel system Y Zhou, Y Yu, W Dai, Y Liang, E Xing Artificial Intelligence and Statistics, 713-722, 2016 | 34 | 2016 |
| Characterization of gradient dominance and regularity conditions for neural networks Y Zhou, Y Liang arXiv preprint arXiv:1710.06910, 2017 | 29 | 2017 |
| Distributed machine learning via sufficient factor broadcasting P Xie, JK Kim, Y Zhou, Q Ho, A Kumar, Y Yu, E Xing arXiv preprint arXiv:1511.08486, 2015 | 27 | 2015 |
| Cubic regularization with momentum for nonconvex optimization Z Wang, Y Zhou, Y Liang, G Lan Uncertainty in Artificial Intelligence, 313-322, 2020 | 25 | 2020 |
| Geometrical properties and accelerated gradient solvers of non-convex phase retrieval Y Zhou, H Zhang, Y Liang 2016 54th Annual Allerton Conference on Communication, Control, and …, 2016 | 24 | 2016 |
| Distributed proximal gradient algorithm for partially asynchronous computer clusters Y Zhou, Y Liang, Y Yu, W Dai, EP Xing The Journal of Machine Learning Research 19 (1), 733-764, 2018 | 23 | 2018 |
| Learning latent space models with angular constraints P Xie, Y Deng, Y Zhou, A Kumar, Y Yu, J Zou, EP Xing International Conference on Machine Learning, 3799-3810, 2017 | 23 | 2017 |
| Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. P Xie, JK Kim, Y Zhou, Q Ho, A Kumar, Y Yu, EP Xing UAI, 2016 | 23 | 2016 |
Yingbin LiangThe Ohio State UniversityVerified email at osu.edu
