| Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Z Li, D Kovalev, X Qian, P Richtárik International Conference on Machine Learning (ICML 2020), 2020 | 80 | 2020 |
| A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization Z Li, J Li Neural Information Processing Systems (NeurIPS 2018), 2018 | 69 | 2018 |
| A unified variance-reduced accelerated gradient method for convex optimization G Lan*, Z Li*, Y Zhou* Neural Information Processing Systems (NeurIPS 2019), 2019 | 56 | 2019 |
| On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs W Cao, J Li, Y Tao, Z Li Neural Information Processing Systems (NIPS 2015), 2015 | 47 | 2015 |
| Learning Two-layer Neural Networks with Symmetric Inputs R Ge*, R Kuditipudi*, Z Li*, X Wang* International Conference on Learning Representations (ICLR 2019), 2019 | 40 | 2019 |
| PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization Z Li, H Bao, X Zhang, P Richtárik International Conference on Machine Learning (ICML 2021), 2020 | 37 | 2020 |
| MARINA: Faster Non-Convex Distributed Learning with Compression E Gorbunov, K Burlachenko, Z Li, P Richtárik International Conference on Machine Learning (ICML 2021), 2021 | 31 | 2021 |
| Optimal in-place suffix sorting Z Li, J Li, H Huo Information and Computation, 2021, 2021 | 27 | 2021 |
| SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points Z Li Neural Information Processing Systems (NeurIPS 2019), 2019 | 27 | 2019 |
| A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization Z Li, P Richtárik arXiv preprint arXiv:2006.07013, 77, 2020 | 26 | 2020 |
| Gradient Boosting With Piece-Wise Linear Regression Trees Y Shi, J Li, Z Li International Joint Conference on Artificial Intelligence (IJCAI 2019), 2019 | 21 | 2019 |
| Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization R Ge*, Z Li*, W Wang*, X Wang* Conference on Learning Theory (COLT 2019), 2019 | 20 | 2019 |
| ZeroSARAH: Efficient nonconvex finite-sum optimization with zero full gradient computation Z Li, S Hanzely, P Richtárik arXiv preprint arXiv:2103.01447, 2021 | 16 | 2021 |
| Stochastic gradient hamiltonian monte carlo with variance reduction for bayesian inference Z Li, T Zhang, S Cheng, J Zhu, J Li Machine Learning 108 (8), 1701-1727, 2019 | 16 | 2019 |
| A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization Z Li, J Li International Conference on Artificial Intelligence and Statistics (AISTATS'20), 2020 | 14* | 2020 |
| FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning H Zhao, Z Li, P Richtárik arXiv preprint arXiv:2108.04755, 2021 | 13 | 2021 |
| EF21 with bells & whistles: Practical algorithmic extensions of modern error feedback I Fatkhullin, I Sokolov, E Gorbunov, Z Li, P Richtárik arXiv preprint arXiv:2110.03294, 2021 | 12 | 2021 |
| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression Z Li, P Richtárik Neural Information Processing Systems (NeurIPS 2021), 2021 | 8 | 2021 |
| BEER: Fast Rate for Decentralized Nonconvex Optimization with Communication Compression H Zhao, B Li, Z Li, P Richtárik, Y Chi arXiv preprint arXiv:2201.13320, 2022 | 3 | 2022 |
| ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Z Li arXiv preprint arXiv:2103.11333, 2021 | 3 | 2021 |
