| Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent X Lian, C Zhang, H Zhang, CJ Hsieh, W Zhang, J Liu Advances in Neural Information Processing Systems, 5330-5340, 2017 | 321 | 2017 |
| Asynchronous parallel stochastic gradient for nonconvex optimization X Lian, Y Huang, Y Li, J Liu Advances in Neural Information Processing Systems, 2737-2745, 2015 | 309 | 2015 |
| Staleness-aware Async-SGD for Distributed Deep Learning W Zhang, S Gupta, X Lian, J Liu International Joint Conference on Artificial Intelligence, 2016 | 164 | 2016 |
| Asynchronous decentralized parallel stochastic gradient descent X Lian, W Zhang, C Zhang, J Liu International Conference on Machine Learning, 3043-3052, 2018 | 144 | 2018 |
| D: Decentralized Training over Decentralized Data H Tang, X Lian, M Yan, C Zhang, J Liu arXiv preprint arXiv:1803.07068, 2018 | 98 | 2018 |
| A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order X Lian, H Zhang, CJ Hsieh, Y Huang, J Liu Advances in Neural Information Processing Systems, 2016 | 57 | 2016 |
| Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression H Tang, C Yu, X Lian, T Zhang, J Liu International Conference on Machine Learning, 6155-6165, 2019 | 56 | 2019 |
| Finite-sum Composition Optimization via Variance Reduced Gradient Descent X Lian, M Wang, J Liu Artificial Intelligence and Statistics, 2017 | 49 | 2017 |
| Asynchronous Parallel Greedy Coordinate Descent Y You*, X Lian*(equal contribution), J Liu, HF Yu, I Dhillon, J Demmel, ... Advances in Neural Information Processing Systems, 2016 | 40 | 2016 |
| Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization X Lian, J Liu The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 11 | 2019 |
| NMR evidence for field-induced ferromagnetism in (Li 0.8 Fe 0.2) OHFeSe superconductor YP Wu, D Zhao, XR Lian, XF Lu, NZ Wang, XG Luo, XH Chen, T Wu Physical Review B 91 (12), 125107, 2015 | 10 | 2015 |
| Efficient smooth non-convex stochastic compositional optimization via stochastic recursive gradient descent W Hu, CJ Li, X Lian, J Liu, H Yuan Advances in Neural Information Processing Systems, 6929-6937, 2019 | 5 | 2019 |
| Staleness-aware Async-SGD for Distributed Deep Learning. CoRR abs/1511.05950 (2015) W Zhang, S Gupta, X Lian, J Liu | 5 | 2015 |
| Stochastic Recursive Momentum for Policy Gradient Methods H Yuan, X Lian, J Liu, Y Zhou arXiv preprint arXiv:2003.04302, 2020 | 3 | 2020 |
| Revisit Batch Normalization: New Understanding from an Optimization View and a Refinement via Composition Optimization X Lian, J Liu arXiv preprint arXiv:1810.06177, 2018 | 2 | 2018 |
| APMSqueeze: A Communication Efficient Adam-Preconditioned Momentum SGD Algorithm H Tang, S Gan, S Rajbhandari, X Lian, C Zhang, J Liu, Y He arXiv preprint arXiv:2008.11343, 2020 | | 2020 |
| Stochastic Recursive Variance Reduction for Efficient Smooth Non-Convex Compositional Optimization H Yuan, X Lian, J Liu arXiv preprint arXiv:1912.13515, 2019 | | 2019 |
| : Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression H Tang, X Lian, S Qiu, L Yuan, C Zhang, T Zhang, J Liu arXiv preprint arXiv:1907.07346, 2019 | | 2019 |
| : Decentralization Meets Error-Compensated Compression H Tang, X Lian, S Qiu, L Yuan, C Zhang, T Zhang, J Liu arXiv, arXiv: 1907.07346, 2019 | | 2019 |
| Large Scale Optimization for Deep Learning X Lian University of Rochester, 2019 | | 2019 |
Ji LiuUniversity of RochesterVerified email at cs.rochester.edu
