| Smoothing proximal gradient method for general structured sparse learning X Chen, Q Lin, S Kim, JG Carbonell, EP Xing Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial …, 2011 | 492* | 2011 |
| Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning H Rafique, M Liu, Q Lin, T Yang Optimization Methods and Software, 1-35, 2021 | 196 | 2021 |
| A Unified Analysis of Stochastic Momentum Methods for Deep Learning. Y Yan, T Yang, Z Li, Q Lin, Y Yang IJCAI, 2955-2961, 2018 | 172* | 2018 |
| An accelerated proximal coordinate gradient method Q Lin, Z Lu, L Xiao Advances in Neural Information Processing Systems, 3059-3067, 2014 | 145 | 2014 |
| An Accelerated Randomized Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization Q Lin, Z Lu, L Xiao SIAM Journal on Optimization 25 (4), 2244–2273, 2015 | 143* | 2015 |
| Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing X Chen, Q Lin, D Zhou International Conference on Machine Learning, 64-72, 2013 | 143 | 2013 |
| Distributed stochastic variance reduced gradient methods by sampling extra data with replacement JD Lee, Q Lin, T Ma, T Yang The Journal of Machine Learning Research 18 (1), 4404-4446, 2017 | 117* | 2017 |
| An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization Q Lin, L Xiao Computational Optimization and Applications 60 (3), 633–674, 2015 | 101 | 2015 |
| First-order convergence theory for weakly-convex-weakly-concave min-max problems M Liu, H Rafique, Q Lin, T Yang The Journal of Machine Learning Research 22 (1), 7651-7684, 2021 | 95* | 2021 |
| RSG: Beating subgradient method without smoothness and strong convexity T Yang, Q Lin Journal of Machine Learning Research 19 (6), 1−33, 2015 | 81 | 2015 |
| Optimal regularized dual averaging methods for stochastic optimization X Chen, Q Lin, J Pena Advances in neural information processing systems 25, 2012 | 58 | 2012 |
| Stochastic convex optimization: Faster local growth implies faster global convergence Y Xu, Q Lin, T Yang International Conference on Machine Learning, 3821-3830, 2017 | 56* | 2017 |
| Generalized inverse classification MT Lash, Q Lin, N Street, JG Robinson, J Ohlmann Proceedings of the 2017 SIAM International Conference on Data Mining, 162-170, 2017 | 53 | 2017 |
| Sparse latent semantic analysis X Chen, Y Qi, B Bai, Q Lin, JG Carbonell Proceedings of the 2011 SIAM International Conference on Data Mining, 474-485, 2011 | 52 | 2011 |
| ADMM without a fixed penalty parameter: Faster convergence with new adaptive penalization Y Xu, M Liu, Q Lin, T Yang Advances in neural information processing systems 30, 2017 | 48 | 2017 |
| Dscovr: Randomized primal-dual block coordinate algorithms for asynchronous distributed optimization L Xiao, AW Yu, Q Lin, W Chen The Journal of Machine Learning Research 20 (1), 1634-1691, 2019 | 47 | 2019 |
| Optimal epoch stochastic gradient descent ascent methods for min-max optimization Y Yan, Y Xu, Q Lin, W Liu, T Yang Advances in Neural Information Processing Systems 33, 5789-5800, 2020 | 46* | 2020 |
| Block-normalized gradient method: An empirical study for training deep neural network AW Yu, L Huang, Q Lin, R Salakhutdinov, J Carbonell arXiv preprint arXiv:1707.04822, 2017 | 40* | 2017 |
| Statistical decision making for optimal budget allocation in crowd labeling X Chen, Q Lin, D Zhou The Journal of Machine Learning Research 16 (1), 1-46, 2015 | 39 | 2015 |
| Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization Q Lin, R Ma, Y Xu Computational Optimization and Applications 82 (1), 175-224, 2022 | 35* | 2022 |
Tianbao YangTexas A&M UniversityVerified email at tamu.edu
