Jie Liu
Mini-batch semi-stochastic gradient descent in the proximal setting
J Konečný, J Liu, P Richtárik, M Takáč
IEEE Journal of Selected Topics in Signal Processing 10 (2), 242-255, 2016
SARAH: A novel method for machine learning problems using stochastic recursive gradient
LM Nguyen, J Liu, K Scheinberg, M Takáč
International Conference on Machine Learning, 2017
mS2GD: Mini-batch semistochastic gradient descent in the proximal setting
J Konecný, J Liu, P Richtárik, M Takác
arXiv preprint arXiv:1410.4744, 2014
Stochastic recursive gradient algorithm for nonconvex optimization
LM Nguyen, J Liu, K Scheinberg, M Takáč
arXiv preprint arXiv:1705.07261, 2017
A coordinate-descent algorithm for tracking solutions in time-varying optimal power flows
J Liu, J Marecek, A Simonetta, M Takač
2018 Power Systems Computation Conference (PSCC), 1-7, 2018
Hybrid methods in solving alternating-current optimal power flows
J Liu, AC Liddell, J Mareček, M Takáč
IEEE Transactions on Smart Grid 8 (6), 2988-2998, 2017
Projected semi-stochastic gradient descent method with mini-batch scheme under weak strong convexity assumption
J Liu, M Takáč
Modeling and Optimization: Theory and Applications, 95-117, 2016
On the acceleration of l-bfgs with second-order information and stochastic batches
J Liu, Y Rong, M Takác, J Huang
arXiv preprint arXiv:1807.05328, 2018
Anomaly detection in manufacturing systems using structured neural networks
J Liu, J Guo, P Orlik, M Shibata, D Nakahara, S Mii, M Takáč
2018 13th World Congress on Intelligent Control and Automation (WCICA), 175-180, 2018
Recent Advances in Randomized Methods for Big Data Optimization
J Liu
Ph.D. Dissertation, 2019
System and method for predicting power plant operational parameters utilizing artificial neural network deep learning methodologies
J Liu, I Akrotirianakis, A Chakraborty
US Patent App. 14/867,380, 2017
Maintenance event planning using adaptive predictive methodologies
I Akrotirianakis, A Chakraborty, J Liu
US Patent App. 14/849,649, 2017
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