1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2021★ 1 cited
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
Kristine Zhang, Yuanheng Wang, Jianzhun Du +4
Many batch RL health applications first discretize time into fixed intervals. However, this discretization both loses resolution and forces a policy computation at each (potentiall…
cs.LG2019
Regional Tree Regularization for Interpretability in Black Box Models
Mike Wu, Sonali Parbhoo, Michael Hughes +5
The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resembl…