| 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 | 155 | 2020 |
| 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 | 122 | 2020 |
| A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization Z Li, J Li Neural Information Processing Systems (NeurIPS 2018), 2018 | 117 | 2018 |
| MARINA: Faster Non-Convex Distributed Learning with Compression E Gorbunov, K Burlachenko, Z Li, P Richtárik International Conference on Machine Learning (ICML 2021), 2021 | 108 | 2021 |
| A unified variance-reduced accelerated gradient method for convex optimization G Lan*, Z Li*, Y Zhou* Neural Information Processing Systems (NeurIPS 2019), 2019 | 68 | 2019 |
| Learning Two-layer Neural Networks with Symmetric Inputs R Ge*, R Kuditipudi*, Z Li*, X Wang* International Conference on Learning Representations (ICLR 2019), 2019 | 65 | 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 | 62 | 2015 |
| Gradient Boosting With Piece-Wise Linear Regression Trees Y Shi, J Li, Z Li International Joint Conference on Artificial Intelligence (IJCAI 2019), 2019 | 51 | 2019 |
| 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 | 47 | 2021 |
| SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points Z Li Neural Information Processing Systems (NeurIPS 2019), 2019 | 45 | 2019 |
| Optimal in-place suffix sorting Z Li, J Li, H Huo Information and Computation, 2022 [arXiv:1610.08305], 2016 | 45* | 2016 |
| A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization Z Li, P Richtárik arXiv preprint arXiv:2006.07013, 77, 2020 | 43 | 2020 |
| Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization R Ge*, Z Li*, W Wang*, X Wang* Conference on Learning Theory (COLT 2019), 2019 | 35 | 2019 |
| BEER: Fast Rate for Decentralized Nonconvex Optimization with Communication Compression H Zhao, B Li, Z Li, P Richtárik, Y Chi Neural Information Processing Systems (NeurIPS 2022), 2022 | 32 | 2022 |
| SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression Z Li, H Zhao, B Li, Y Chi Neural Information Processing Systems (NeurIPS 2022), 2022 | 31 | 2022 |
| FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning H Zhao, Z Li, P Richtárik arXiv preprint arXiv:2108.04755, 2021 | 30 | 2021 |
| ZeroSARAH: Efficient nonconvex finite-sum optimization with zero full gradient computation Z Li, S Hanzely, P Richtárik arXiv preprint arXiv:2103.01447, 2021 | 28 | 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, 1701-1727, 2019 | 28 | 2019 |
| 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation P Richtárik, I Sokolov, I Fatkhullin, E Gasanov, Z Li, E Gorbunov International Conference on Machine Learning (ICML 2022), 2022 | 27 | 2022 |
| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression Z Li, P Richtárik Neural Information Processing Systems (NeurIPS 2021), 2021 | 27 | 2021 |
Peter RichtarikProfessor, KAUST在 kaust.edu.sa 的电子邮件经过验证
