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Guannan Liang
Title
Cited by
Cited by
Year
Calibrating the adaptive learning rate to improve convergence of ADAM
Q Tong, G Liang, J Bi
Neurocomputing 481, 333-356, 2022
552022
A sparse interactive model for matrix completion with side information
J Lu, G Liang, J Sun, J Bi
Advances in neural information processing systems 29, 2016
392016
Dynamic modeling of power outages caused by thunderstorms
BA Alpay, D Wanik, P Watson, D Cerrai, G Liang, E Anagnostou
Forecasting 2 (2), 151-162, 2020
332020
Effective federated adaptive gradient methods with non-iid decentralized data
Q Tong, G Liang, J Bi
arXiv preprint arXiv:2009.06557, 2020
312020
Differentially private and communication efficient collaborative learning
J Ding, G Liang, J Bi, M Pan
Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7219-7227, 2021
242021
Towards plausible differentially private ADMM based distributed machine learning
J Ding, J Wang, G Liang, J Bi, M Pan
Proceedings of the 29th ACM International Conference on Information …, 2020
152020
Shared neural item representations for completely cold start problem
R Raziperchikolaei, G Liang, Y Chung
Proceedings of the 15th ACM Conference on Recommender Systems, 422-431, 2021
142021
Federated nonconvex sparse learning
Q Tong, G Liang, T Zhu, J Bi
arXiv preprint arXiv:2101.00052, 2020
122020
Calibrating the learning rate for adaptive gradient methods to improve generalization performance
Q Tong, G Liang, J Bi
arXiv preprint arXiv:1908.00700, 2019
112019
Asynchronous parallel stochastic Quasi-Newton methods
Q Tong, G Liang, X Cai, C Zhu, J Bi
Parallel computing 101, 102721, 2021
82021
An effective hard thresholding method based on stochastic variance reduction for nonconvex sparse learning
G Liang, Q Tong, C Zhu, J Bi
Proceedings of the AAAI Conference on Artificial Intelligence 34 (02), 1585-1592, 2020
52020
Federated Optimization of 0-norm Regularized Sparse Learning
Q Tong, G Liang, J Ding, T Zhu, M Pan, J Bi
Algorithms 15 (9), 319, 2022
32022
Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods
G Liang, Q Tong, C Zhu, J Bi
arXiv preprint arXiv:2103.04413, 2021
32021
Stochastic privacy-preserving methods for nonconvex sparse learning
G Liang, Q Tong, J Ding, M Pan, J Bi
Information Sciences 630, 567-585, 2023
22023
An Effective Tensor Regression with Latent Sparse Regularization.
K Chen, T Xu, G Liang, Q Tong, M Song, J Bi
Journal of Data Science 20 (2), 2022
12022
Reducing sample selection bias in a machine learning-based recommender system
Y Shi, G Liang, Y Chung
US Patent 11,995,665, 2024
2024
Reducing sample selection bias in a machine learning-based recommender system
Y Shi, G Liang, Y Chung
US Patent App. 17/713,855, 2023
2023
Reducing sample selection bias in a machine learning-based recommender system
SHI YANG, L GUANNAN, C YOUNGJOO
US Patent US20230033492A1, 2023
2023
Reducing sample selection bias in a machine learning-based recommender system
SHI YANG, L GUANNAN, C YOUNGJOO
US Patent US2023036394A1, 2023
2023
Meta-Shop: Improving Item Advertisement For Small Businesses
Y Shi, G Liang, Y Chung
arXiv preprint arXiv:2212.01414, 2022
2022
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