8 citations · 16 across the 3 of their papers we have counts for
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cs.LG2022★ 1 cited
Provable Safe Reinforcement Learning with Binary Feedback
Andrew Bennett, Dipendra Misra, Nathan Kallus
Safety is a crucial necessity in many applications of reinforcement learning (RL), whether robotic, automotive, or medical. Many existing approaches to safe RL rely on receiving nu…
cs.LG2020★ 8 cited
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Andrew Bennett, Nathan Kallus, Lihong Li +1
Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings where experimentation is limited, such as education and healthcare. But, in these very sam…
cs.LG2020★ 7 cited
Efficient Policy Learning from Surrogate-Loss Classification Reductions
Andrew Bennett, Nathan Kallus
Recent work on policy learning from observational data has highlighted the importance of efficient policy evaluation and has proposed reductions to weighted (cost-sensitive) classi…