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Year
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
1622020
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
34*2020
An optimal hybrid variance-reduced algorithm for stochastic composite nonconvex optimization
D Liu, LM Nguyen, Q Tran-Dinh
arXiv preprint arXiv:2008.09055, 2020
132020
A Newton Frank–Wolfe method for constrained self-concordant minimization
D Liu, V Cevher, Q Tran-Dinh
Journal of Global Optimization, 1-27, 2022
112022
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
32020
Efficient and Provable Algorithms for Convex Optimization Problems Beyond Lipschitz Continuous Gradients
D Liu
The University of North Carolina at Chapel Hill, 2022
2022
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