8 citations · 8 across the 2 of their papers we have counts for
3 papers
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
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
Ali Mousavi, Lihong Li, Qiang Liu +1
Off-policy estimation for long-horizon problems is important in many real-life applications such as healthcare and robotics, where high-fidelity simulators may not be available and…
stat.ML2017
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk
In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learn…