Segui
Zirui Zhou
Titolo
Citata da
Citata da
Anno
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
4132021
A unified approach to error bounds for structured convex optimization problems
Z Zhou, AMC So
Mathematical Programming 165, 689-728, 2017
1772017
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
752013
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
642013
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
57*2018
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
492018
\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
482015
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
452017
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
442022
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
372021
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
272019
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
262021
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
262019
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
232022
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
212020
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
202021
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
162014
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
132022
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
122021
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
92023
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