| Personalized cross-silo federated learning on non-iid data Y Huang, L Chu, Z Zhou, L Wang, J Liu, J Pei, Y Zhang Proceedings of the AAAI conference on artificial intelligence 35 (9), 7865-7873, 2021 | 588 | 2021 |
| A unified approach to error bounds for structured convex optimization problems Z Zhou, AMC So Mathematical Programming 165, 689-728, 2017 | 185 | 2017 |
| Beyond convex relaxation: A polynomial-time non-convex optimization approach to network localization S Ji, KF Sze, Z Zhou, AMC So, Y Ye 2013 Proceedings IEEE INFOCOM, 2499-2507, 2013 | 78 | 2013 |
| Improving fairness for data valuation in horizontal federated learning Z Fan, H Fang, Z Zhou, J Pei, MP Friedlander, C Liu, Y Zhang 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2440-2453, 2022 | 65 | 2022 |
| A family of inexact SQA methods for non-smooth convex minimization with provable convergence guarantees based on the Luo–Tseng error bound property MC Yue, Z Zhou, AMC So Mathematical Programming, 1-32, 2018 | 65* | 2018 |
| On the linear convergence of the proximal gradient method for trace norm regularization K Hou, Z Zhou, AMC So, ZQ Luo Advances in Neural Information Processing Systems 26, 2013 | 64 | 2013 |
| Enhanced proximal DC algorithms with extrapolation for a class of structured nonsmooth DC minimization Z Lu, Z Zhou, Z Sun Mathematical Programming, 1-33, 2018 | 55 | 2018 |
| \ell_1, p-Norm Regularization: Error Bounds and Convergence Rate Analysis of First-Order Methods Z Zhou, Q Zhang, AMC So International conference on machine learning, 1501-1510, 2015 | 53 | 2015 |
| Fedfair: Training fair models in cross-silo federated learning L Chu, L Wang, Y Dong, J Pei, Z Zhou, Y Zhang arXiv preprint arXiv:2109.05662, 2021 | 48 | 2021 |
| Non-asymptotic convergence analysis of inexact gradient methods for machine learning without strong convexity AMC So, Z Zhou Optimization Methods and Software 32 (4), 963-992, 2017 | 48 | 2017 |
| Towards fair federated learning Z Zhou, L Chu, C Liu, L Wang, J Pei, Y Zhang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 42 | 2021 |
| Fair and efficient contribution valuation for vertical federated learning Z Fan, H Fang, Z Zhou, J Pei, MP Friedlander, Y Zhang arXiv preprint arXiv:2201.02658, 2022 | 38 | 2022 |
| Nonmonotone enhanced proximal DC algorithms for a class of structured nonsmooth DC programming Z Lu, Z Zhou SIAM Journal on Optimization 29 (4), 2725-2752, 2019 | 34 | 2019 |
| On the quadratic convergence of the cubic regularization method under a local error bound condition MC Yue, Z Zhou, A Man-Cho So SIAM Journal on Optimization 29 (1), 904-932, 2019 | 30 | 2019 |
| Personalized federated learning: An attentive collaboration approach Y Huang, L Chu, Z Zhou, L Wang, J Liu, J Pei, Y Zhang arXiv preprint arXiv:2007.03797 1, 2, 2020 | 23 | 2020 |
| Optimal non-convex exact recovery in stochastic block model via projected power method P Wang, H Liu, Z Zhou, AMC So International Conference on Machine Learning, 10828-10838, 2021 | 21 | 2021 |
| Augmenting operations research with auto-formulation of optimization models from problem descriptions R Ramamonjison, H Li, TT Yu, S He, V Rengan, A Banitalebi-Dehkordi, ... arXiv preprint arXiv:2209.15565, 2022 | 19 | 2022 |
| Achieving model fairness in vertical federated learning C Liu, Z Fan, Z Zhou, Y Shi, J Pei, L Chu, Y Zhang arXiv preprint arXiv:2109.08344, 2021 | 16 | 2021 |
| Latent aspect mining via exploring sparsity and intrinsic information Y Xu, T Lin, W Lam, Z Zhou, H Cheng, AMC So Proceedings of the 23rd ACM international conference on conference on …, 2014 | 16 | 2014 |
| Nl4opt competition: Formulating optimization problems based on their natural language descriptions R Ramamonjison, T Yu, R Li, H Li, G Carenini, B Ghaddar, S He, ... NeurIPS 2022 Competition Track, 189-203, 2023 | 13 | 2023 |
Yong ZhangDistinguished Researcher at Huawei Technologies, Canada在 huawei.com 的电子邮件经过验证
