Anarchic federated learning H Yang, X Zhang, P Khanduri, J Liu International Conference on Machine Learning, 25331-25363, 2022 | 80 | 2022 |
Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach H Yang, X Zhang, M Fang, J Liu 2019 IEEE 58th Conference on Decision and Control (CDC), 5832-5837, 2019 | 53 | 2019 |
Compressed distributed gradient descent: Communication-efficient consensus over networks X Zhang, J Liu, Z Zhu, ES Bentley IEEE INFOCOM 2019-IEEE Conference on Computer Communications, 2431-2439, 2019 | 38 | 2019 |
Drug–target interaction prediction by integrating multiview network data X Zhang, L Li, MK Ng, S Zhang Computational biology and chemistry 69, 185-193, 2017 | 37 | 2017 |
Taming communication and sample complexities in decentralized policy evaluation for cooperative multi-agent reinforcement learning X Zhang, Z Liu, J Liu, Z Zhu, S Lu Advances in Neural Information Processing Systems 34, 18825-18838, 2021 | 31 | 2021 |
Private and communication-efficient edge learning: a sparse differential gaussian-masking distributed SGD approach X Zhang, M Fang, J Liu, Z Zhu Proceedings of the Twenty-First International Symposium on Theory …, 2020 | 30 | 2020 |
Taming convergence for asynchronous stochastic gradient descent with unbounded delay in non-convex learning X Zhang, J Liu, Z Zhu 2020 59th IEEE Conference on Decision and Control (CDC), 3580-3585, 2020 | 25 | 2020 |
Learning coefficient heterogeneity over networks: A distributed spanning-tree-based fused-lasso regression X Zhang, J Liu, Z Zhu Journal of the American Statistical Association 119 (545), 485-497, 2024 | 24* | 2024 |
NET-FLEET: Achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data X Zhang, M Fang, Z Liu, H Yang, J Liu, Z Zhu Proceedings of the Twenty-Third International Symposium on Theory …, 2022 | 16 | 2022 |
Interact: Achieving low sample and communication complexities in decentralized bilevel learning over networks Z Liu, X Zhang, P Khanduri, S Lu, J Liu Proceedings of the Twenty-Third International Symposium on Theory …, 2022 | 15 | 2022 |
SAGDA: Achieving Communication Complexity in Federated Min-Max Learning H Yang, Z Liu, X Zhang, J Liu Advances in Neural Information Processing Systems 35, 7142-7154, 2022 | 14 | 2022 |
GT-STORM: Taming sample, communication, and memory complexities in decentralized non-convex learning X Zhang, J Liu, Z Zhu, ES Bentley Proceedings of the Twenty-second International Symposium on Theory …, 2021 | 13 | 2021 |
Electricity consumer archetypes study based on functional data analysis and K-means algorithm Z Xin, G Weiguo, SU Yun Power system technology 39 (2), 3153-3162, 2015 | 12* | 2015 |
Fast and robust sparsity learning over networks: A decentralized surrogate median regression approach W Liu, X Mao, X Zhang IEEE Transactions on Signal Processing 70, 797-809, 2022 | 10 | 2022 |
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models Q Tian, X Zhang, J Zhao Proc. ICML 2023, 2023 | 9 | 2023 |
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities Z Liu, X Zhang, S Lu, J Liu Mobihoc 2023, 2023 | 9 | 2023 |
Low sample and communication complexities in decentralized learning: A triple hybrid approach X Zhang, J Liu, Z Zhu, ES Bentley IEEE INFOCOm 2021-IEEE conference on computer communications, 1-10, 2021 | 7 | 2021 |
Clustered coefficient regression models for poisson process with an application to seasonal warranty claim data X Wang, X Zhang, Z Zhu Technometrics 65 (4), 514-523, 2023 | 6 | 2023 |
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning Z Liu, X Zhang, P Khanduri, S Lu, J Liu Proc. ICML 2023, 2023 | 6 | 2023 |
Communication-efficient network-distributed optimization with differential-coded compressors X Zhang, J Liu, Z Zhu, ES Bentley IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 317-326, 2020 | 5 | 2020 |