23 citations · 48 across the 12 of their papers we have counts for
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cs.LG2023
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
Shengpu Tang, Jenna Wiens
In applying reinforcement learning (RL) to high-stakes domains, quantitative and qualitative evaluation using observational data can help practitioners understand the generalizatio…
cs.LG2023
Leveraging Factored Action Spaces for Off-Policy Evaluation
Aaman Rebello, Shengpu Tang, Jenna Wiens +1
Off-policy evaluation (OPE) aims to estimate the benefit of following a counterfactual sequence of actions, given data collected from executed sequences. However, existing OPE esti…
cs.LG2023★ 4 cited
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Shengpu Tang, Maggie Makar, Michael W. Sjoding +2
Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent fact…