| 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 | 201 | 2019 |
| Reshaped Wirtinger Flow and Incremental Algorithms for Solving Quadratic Systems of Equations H Zhang, Y Zhou, Y Liang, Y Chi arXiv preprint, 0 | 189* | |
| Ibm federated learning: an enterprise framework white paper v0. 1 H Ludwig, N Baracaldo, G Thomas, Y Zhou, A Anwar, S Rajamoni, Y Ong, ... arXiv preprint arXiv:2007.10987, 2020 | 183 | 2020 |
| A nonconvex approach for phase retrieval: Reshaped wirtinger flow and incremental algorithms H Zhang, Y Liang, Y Chi Journal of Machine Learning Research 18 (141), 1-35, 2017 | 159 | 2017 |
| Critical points of linear neural networks: Analytical forms and landscape properties Y Zhou, Y Liang ICLR2018, 2018 | 107* | 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 | 100 | 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 | 97 | 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 | 87 | 2019 |
| Spiderboost: A class of faster variance-reduced algorithms for nonconvex optimization Z Wang, K Ji, Y Zhou, Y Liang, V Tarokh arXiv 2018, 2018 | 87 | 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 | 82 | 2018 |
| 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 | 67 | 2020 |
| 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 | 66 | 2019 |
| Reanalysis of variance reduced temporal difference learning T Xu, Z Wang, Y Zhou, Y Liang arXiv preprint arXiv:2001.01898, 2020 | 52 | 2020 |
| A new one-point residual-feedback oracle for black-box learning and control Y Zhang, Y Zhou, K Ji, MM Zavlanos Automatica 136, 110006, 2022 | 45 | 2022 |
| 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 | 39 | 2016 |
| Generalized-smooth nonconvex optimization is as efficient as smooth nonconvex optimization Z Chen, Y Zhou, Y Liang, Z Lu International Conference on Machine Learning, 5396-5427, 2023 | 37 | 2023 |
| Characterization of gradient dominance and regularity conditions for neural networks Y Zhou, Y Liang arXiv preprint arXiv:1710.06910, 2017 | 37 | 2017 |
| Proximal gradient descent-ascent: Variable convergence under k {\L} geometry Z Chen, Y Zhou, T Xu, Y Liang arXiv preprint arXiv:2102.04653, 2021 | 36 | 2021 |
| Sample and communication-efficient decentralized actor-critic algorithms with finite-time analysis Z Chen, Y Zhou, RR Chen, S Zou International Conference on Machine Learning, 3794-3834, 2022 | 34 | 2022 |
| 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 | 34 | 2015 |
Yingbin LiangThe Ohio State UniversityVerificeret mail på osu.edu