Wenqing Hu
Wenqing Hu
Missouri University of Science and Technology (formerly University of Missouri, Rolla)
Verified email at - Homepage
Cited by
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On the diffusion approximation of nonconvex stochastic gradient descent
W Hu, CJ Li, L Li, JG Liu
arXiv preprint arXiv:1705.07562, 2017
On the noisy gradient descent that generalizes as sgd
J Wu, W Hu, H Xiong, J Huan, V Braverman, Z Zhu
International Conference on Machine Learning, 10367-10376, 2020
Human mobility synchronization and trip purpose detection with mixture of hawkes processes
P Wang, Y Fu, G Liu, W Hu, C Aggarwal
Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017
Real-time frequency regulation using aggregated electric vehicles in smart grid
MM Islam, X Zhong, Z Sun, H Xiong, W Hu
Computers & Industrial Engineering 134, 11-26, 2019
Smoluchowski-Kramers approximation in the case of variable friction
M Freidlin, W Hu
arXiv preprint arXiv:1203.0603, 2012
Efficient smooth non-convex stochastic compositional optimization via stochastic recursive gradient descent
W Hu, CJ Li, X Lian, J Liu, H Yuan
Advances in Neural Information Processing Systems 32, 2019
On the fast convergence of random perturbations of the gradient flow
J Yang, W Hu, CJ Li
arXiv preprint arXiv:1706.00837, 2017
Small mass asymptotic for the motion with vanishing friction
M Freidlin, W Hu, A Wentzell
Stochastic Processes and their Applications 123 (1), 45-75, 2013
Large deviations and averaging for systems of slow-fast stochastic reaction–diffusion equations
W Hu, M Salins, K Spiliopoulos
Stochastics and Partial Differential Equations: Analysis and Computations 7 …, 2019
Hypoelliptic multiscale Langevin diffusions: large deviations, invariant measures and small mass asymptotics
W Hu, K Spiliopoulos
On perturbations of generalized Landau-Lifshitz dynamics
M Freidlin, W Hu
Journal of Statistical Physics 144, 978-1008, 2011
Stochastic Recursive Momentum Method for Non-Convex Compositional Optimization
H Yuan, W Hu
arXiv preprint arXiv:2006.01688, 2020
Joint Control of Manufacturing and Onsite Microgrid System via Novel Neural-Network Integrated Reinforcement Learning Algorithms
J Yang, Z Sun, W Hu, L Steimeister
Applied Energy (accepted), 2022
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent
W Hu, Z Zhu, H Xiong, J Huan
arXiv preprint arXiv:1901.06054, 2019
On diffusion in narrow random channels
M Freidlin, W Hu
Journal of Statistical Physics 152, 136-158, 2013
Joint manufacturing and onsite microgrid system control using markov decision process and neural network integrated reinforcement learning
W Hu, Z Sun, Y Zhang, Y Li
Procedia Manufacturing 39, 1242-1249, 2019
: Lowering the Bound of Misclassification Rate for Sparse Linear Discriminant Analysis via Model Debiasing
H Xiong, W Cheng, J Bian, W Hu, Z Sun, Z Guo
IEEE transactions on neural networks and learning systems 30 (3), 707-717, 2018
De-biasing Covariance-Regularized Discriminant Analysis.
H Xiong, W Cheng, Y Fu, W Hu, J Bian, Z Guo
IJCAI, 2889-2897, 2018
A convergence analysis of the perturbed compositional gradient flow: Averaging principle and normal deviations
CJ Hu, Wenqing, Li
Discrete & Continuous Dynamical Systems-A 38 (10), 4951-4977, 2018
AWDA: An Adaptive Wishart Discriminant Analysis
H Xiong, W Cheng, W Hu, J Bian, Z Guo
ICDM 2017 (2017 IEEE International Conference on Data Mining), New Orleans …, 2017
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