56 citations · 65 across the 3 of their papers we have counts for
3 papers
stat.ML2021★ 7 cited
abess: A Fast Best Subset Selection Library in Python and R
Jin Zhu, Xueqin Wang, Liyuan Hu +5
We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classifi…
q-fin.CP2019★ 2 cited
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data
Junming Yang, Yaoqi Li, Xuanyu Chen +2
Training a practical and effective model for stock selection has been a greatly concerned problem in the field of artificial intelligence. Even though some of the models from previ…
q-fin.PM2018★ 56 cited
Adversarial Deep Reinforcement Learning in Portfolio Management
Zhipeng Liang, Hao Chen, Junhao Zhu +2
In this paper, we implement three state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Polic…