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2020 – today
- 2023
[j4]Guannan Liang, Qianqian Tong
, Jiahao Ding, Miao Pan, Jinbo Bi:
Stochastic privacy-preserving methods for nonconvex sparse learning. Inf. Sci. 630: 567-585 (2023)- 2022
- [j3]Qianqian Tong, Guannan Liang, Jiahao Ding, Tan Zhu, Miao Pan, Jinbo Bi:
Federated Optimization of ℓ0-norm Regularized Sparse Learning. Algorithms 15(9): 319 (2022) - [j2]Qianqian Tong, Guannan Liang, Jinbo Bi:
Calibrating the adaptive learning rate to improve convergence of ADAM. Neurocomputing 481: 333-356 (2022) - [i9]Yang Shi, Guannan Liang, Young-joo Chung:
Meta-Shop: Improving Item Advertisement For Small Businesses. CoRR abs/2212.01414 (2022) - 2021
- [j1]Qianqian Tong, Guannan Liang, Xingyu Cai, Chunjiang Zhu, Jinbo Bi:
Asynchronous parallel stochastic Quasi-Newton methods. Parallel Comput. 101: 102721 (2021) - [c7]Jiahao Ding, Guannan Liang, Jinbo Bi, Miao Pan:
Differentially Private and Communication Efficient Collaborative Learning. AAAI 2021: 7219-7227 - [c6]Tan Zhu, Guannan Liang, Chunjiang Zhu, Haining Li, Jinbo Bi:
An Efficient Algorithm for Deep Stochastic Contextual Bandits. AAAI 2021: 11193-11201 - [c5]Ramin Raziperchikolaei, Guannan Liang, Young-joo Chung:
Shared Neural Item Representations for Completely Cold Start Problem. RecSys 2021: 422-431 - [i8]Qianqian Tong, Guannan Liang, Tan Zhu, Jinbo Bi:
Federated Nonconvex Sparse Learning. CoRR abs/2101.00052 (2021) - [i7]Guannan Liang, Qianqian Tong, Chunjiang Zhu, Jinbo Bi:
Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods. CoRR abs/2103.04413 (2021) - [i6]Tan Zhu, Guannan Liang, Chunjiang Zhu, Haining Li, Jinbo Bi:
An Efficient Algorithm for Deep Stochastic Contextual Bandits. CoRR abs/2104.05613 (2021) - 2020
- [c4]Guannan Liang, Qianqian Tong, Chunjiang Zhu, Jinbo Bi:
An Effective Hard Thresholding Method Based on Stochastic Variance Reduction for Nonconvex Sparse Learning. AAAI 2020: 1585-1592 - [c3]Jiahao Ding, Jingyi Wang, Guannan Liang, Jinbo Bi, Miao Pan:
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning. CIKM 2020: 285-294 - [c2]Guannan Liang, Qianqian Tong, Jiahao Ding, Miao Pan, Jinbo Bi:
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning. ICDM 2020: 342-351 - [i5]Jiahao Ding, Jingyi Wang, Guannan Liang, Jinbo Bi, Miao Pan:
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning. CoRR abs/2008.04500 (2020) - [i4]Qianqian Tong, Guannan Liang, Jinbo Bi:
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data. CoRR abs/2009.06557 (2020) - [i3]Guannan Liang, Qianqian Tong, Jiahao Ding, Miao Pan, Jinbo Bi:
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning. CoRR abs/2009.06562 (2020) - [i2]Qianqian Tong, Guannan Liang, Xingyu Cai, Chunjiang Zhu, Jinbo Bi:
Asynchronous Parallel Stochastic Quasi-Newton Methods. CoRR abs/2011.00667 (2020)
2010 – 2019
- 2019
- [i1]Qianqian Tong, Guannan Liang, Jinbo Bi:
Calibrating the Learning Rate for Adaptive Gradient Methods to Improve Generalization Performance. CoRR abs/1908.00700 (2019) - 2016
- [c1]Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi:
A Sparse Interactive Model for Matrix Completion with Side Information. NIPS 2016: 4071-4079
Coauthor Index
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