| Calibrating the adaptive learning rate to improve convergence of ADAM Q Tong, G Liang, J Bi Neurocomputing 481, 333-356, 2022 | 53 | 2022 |
| 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 | 38 | 2016 |
| 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 | 33 | 2020 |
| Effective federated adaptive gradient methods with non-iid decentralized data Q Tong, G Liang, J Bi arXiv preprint arXiv:2009.06557, 2020 | 30 | 2020 |
| 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 | 21 | 2021 |
| 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 | 14 | 2020 |
| 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 | 12 | 2021 |
| Federated nonconvex sparse learning Q Tong, G Liang, T Zhu, J Bi arXiv preprint arXiv:2101.00052, 2020 | 12 | 2020 |
| Calibrating the learning rate for adaptive gradient methods to improve generalization performance Q Tong, G Liang, J Bi arXiv preprint arXiv:1908.00700, 2019 | 11 | 2019 |
| Asynchronous parallel stochastic Quasi-Newton methods Q Tong, G Liang, X Cai, C Zhu, J Bi Parallel computing 101, 102721, 2021 | 8 | 2021 |
| 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 | 5 | 2020 |
| Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods G Liang, Q Tong, C Zhu, J Bi arXiv preprint arXiv:2103.04413, 2021 | 3 | 2021 |
| Stochastic privacy-preserving methods for nonconvex sparse learning G Liang, Q Tong, J Ding, M Pan, J Bi Information Sciences 630, 567-585, 2023 | 2 | 2023 |
| Federated Optimization of ℓ0-norm Regularized Sparse Learning Q Tong, G Liang, J Ding, T Zhu, M Pan, J Bi Algorithms 15 (9), 319, 2022 | 2 | 2022 |
| 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 | 1 | 2022 |
| 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 |
| An automated decadal survey of saguaro population using deep learning X Shen, G Liang, X Feng, A Allen AGU Fall Meeting Abstracts 2021, B25E-1496, 2021 | | 2021 |
| An Efficient Algorithm for Deep Stochastic Contextual Bandits T Zhu, G Liang, C Zhu, H Li, J Bi Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 11193 …, 2021 | | 2021 |
