| Robust and generalizable visual representation learning via random convolutions Z Xu, D Liu, J Yang, C Raffel, M Niethammer arXiv preprint arXiv:2007.13003, 2020 | 190 | 2020 |
| Hybrid variance-reduced sgd algorithms for minimax problems with nonconvex-linear function Q Tran Dinh, D Liu, L Nguyen Advances in Neural Information Processing Systems 33, 11096-11107, 2020 | 37* | 2020 |
| A Newton Frank–Wolfe method for constrained self-concordant minimization D Liu, V Cevher, Q Tran-Dinh Journal of Global Optimization, 1-27, 2022 | 15 | 2022 |
| An optimal hybrid variance-reduced algorithm for stochastic composite nonconvex optimization D Liu, LM Nguyen, Q Tran-Dinh arXiv preprint arXiv:2008.09055, 2020 | 14 | 2020 |
| New Primal-Dual Algorithms for a Class of Nonsmooth and Nonlinear Convex-Concave Minimax Problems Y Zhu, D Liu, Q Tran-Dinh SIAM Journal on Optimization 32 (4), 2580-2611, 2022 | 8* | 2022 |
| A new randomized primal-dual algorithm for convex optimization with fast last iterate convergence rates Q Tran-Dinh, D Liu Optimization Methods and Software 38 (1), 184-217, 2023 | 5* | 2023 |
| An inexact interior-point Lagrangian decomposition algorithm with inexact oracles D Liu, Q Tran-Dinh Journal of Optimization Theory and Applications 185 (3), 903-926, 2020 | 3 | 2020 |
| Efficient and Provable Algorithms for Convex Optimization Problems Beyond Lipschitz Continuous Gradients D Liu The University of North Carolina at Chapel Hill, 2022 | | 2022 |
Quoc Tran-DinhDepartment of Statistics and Operations Research, UNC在 email.unc.edu 的电子邮件经过验证
