A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning

no code implementations • 20 May 2021 • Yongfeng Li, Mingming Zhao, WeiJie Chen, Zaiwen Wen

A general theoretical analysis shows that the solutions generated from a sequence of the constrained optimizations converge to the optimal solution of the LP if the error is controlled properly.

reinforcement-learning Reinforcement Learning (RL)

Precisely Predicting Acute Kidney Injury with Convolutional Neural Network Based on Electronic Health Record Data

no code implementations • 27 May 2020 • Yu Wang, Junpeng Bao, JianQiang Du, Yongfeng Li

Compared with the existing AKI predictors, the predictor in this work greatly improves the precision of early prediction of AKI by using the Convolutional Neural Network architecture and a more concise input vector.