Qihang Lin
Title
Cited by
Cited by
Year
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
461*2011
Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing
X Chen, Q Lin, D Zhou
International Conference on Machine Learning, 64-72, 2013
1342013
An accelerated proximal coordinate gradient method
Q Lin, Z Lu, L Xiao
Advances in Neural Information Processing Systems, 3059-3067, 2014
1332014
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
1152021
A Unified Analysis of Stochastic Momentum Methods for Deep Learning.
Y Yan, T Yang, Z Li, Q Lin, Y Yang
IJCAI, 2955-2961, 2018
110*2018
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
105*2017
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
972015
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
802015
RSG: Beating subgradient method without smoothness and strong convexity
T Yang, Q Lin
arXiv preprint arXiv:1512.03107, 2015
712015
Stochastic convex optimization: Faster local growth implies faster global convergence
Y Xu, Q Lin, T Yang
International Conference on Machine Learning, 3821-3830, 2017
60*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
532011
Solving weakly-convex-weakly-concave saddle-point problems as weakly-monotone variational inequality
Q Lin, M Liu, H Rafique, T Yang
arXiv preprint arXiv:1810.10207 5, 2018
522018
Optimal regularized dual averaging methods for stochastic optimization
X Chen, Q Lin, J Pena
Advances in Neural Information Processing Systems, 395-403, 2012
502012
An accelerated proximal coordinate gradient method and its application to regularized empirical risk minimization
Q Lin, Z Lu, L Xiao
arXiv preprint arXiv:1407.1296, 2014
392014
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
352019
No More Fixed Penalty Parameter in ADMM: Faster Convergence with New Adaptive Penalization
Y Xu, M Liu, T Yang, Q Lin
Advances in Neural Information Processing Systems, 1267-1277, 2017
352017
A sparsity preserving stochastic gradient methods for sparse regression
Q Lin, X Chen, J Peņa
Computational Optimization and Applications 58 (2), 455-482, 2014
33*2014
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
322015
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
292017
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
28*2017
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Articles 1–20