Publications

1. Qianqian Tong, Guannan Liang, Xingyu Cai, Chunjiang Zhu, Jinbo Bi, Asynchronous Parallel Stochastic Quasi-Newton Methods, Parallel Computing Journal (2020)
2. Guannan Liang, Qianqian Tong, Jiahao Ding, Miao Pan and Jinbo Bi, Effective Proximal Methods for Non-convex Non-smooth Regularized Learning, International Conference on Data Mining ( ICDM 2020 )
3. Jiahao Ding, Jingyi Wang, Guannan Liang, Jinbo Bi and Miao Pan, Towards Plausible Differentially Private ADMM Based Distributed Machine Learning, International Conference on Information and Knowledge Mnagement (CIKM 2020)
4. Berk Alpay, David Wanik, Peter Watson, Diego Cerrai, Guannan Liang, and Emmanouil Anagnostou, Dynamic Modeling of Power Outages Caused by Thunderstorms, Forecasting (2020 )
5. Qianqian Tong, Guannan Liang and Jinbo Bi, Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data, arXiv(2020)
6. Guannan Liang, Qianqian Tong, Chunjiang Zhu and Jinbo Bi, An Effective Hard Thresholding Method Based on Stochastic Variance Reduction for Nonconvex Sparse Learning, AAAI Conference on Artificial Intelligence (AAAI 2020)
7. Qianqian Tong, Guannan Liang and Jinbo Bi, Calibrating the Adaptive Learning Rate to Improve Convergence of ADAM , arXiv (2019)
8. Jin Lu, Guannan Liang, Jiangwen Sun and Jinbo Bi , A Sparse Interactive Model for Inductive Matrix Completion, Neural Information Processing Systems( NeurIPS 2016)
9. Shaochun Chen, Guannan Liang and Honru Chen , The Convergence of Zienkiewicz Element Under Un-anisotropic Grid, Mathematica Numerica Sinica (2013)