7 citations · 8 across the 3 of their papers we have counts for
3 papers
stat.ML2024
Off-policy Evaluation with Deeply-abstracted States
Meiling Hao, Pingfan Su, Liyuan Hu +3
Off-policy evaluation (OPE) is crucial for assessing a target policy's impact offline before its deployment. However, achieving accurate OPE in large state spaces remains challengi…
stat.ML2022★ 1 cited
Doubly Inhomogeneous Reinforcement Learning
Liyuan Hu, Mengbing Li, Chengchun Shi +2
This paper studies reinforcement learning (RL) in doubly inhomogeneous environments under temporal non-stationarity and subject heterogeneity. In a number of applications, it is co…
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…