[46] Yao Liu, Pierre-Luc Bacon, and Emma Brunskill. Understanding the curse of horizon in off-policy evaluation via conditional importance sampling. In International Conference on Machine Learning, pages 6184-6193. PMLR, 2020.
[47] James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, Guan Wang, David L Roberts, Matthew E Taylor, and Michael L Littman. Interactive learning from policy-dependent human feedback. In International Conference on Machine Learning, pages 2285-2294. PMLR, 2017.
[48] Travis Mandel, Yun-En Liu, Emma Brunskill, and Zoran Popovic. Where to add actions in human-in-the-loop reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017.
[49] Travis Mandel, Yun-En Liu, Sergey Levine, Emma Brunskill, and Zoran Popovic. Offline policy evaluation across representations with applications to educational games. In AAMAS, volume 1077, 2014.
[50] Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 2243-2251, 2018.
[51] Ofir Nachum, Yinlam Chow, Bo Dai, and Lihong Li. Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections. Advances in Neural Information Processing Systems, 32:2318–2328, 2019.
[52] Vivek Nagaraj, Andrew Lamperski, and Theoden I Netoff. Seizure control in a computational model using a reinforcement learning stimulation paradigm. International Journal of Neural Systems, 27(07):1750012, 2017.
[53] Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, and Emma Brunskill. Off-policy policy evaluation for sequential decisions under unobserved confounding. Advances in Neural Information Processing Systems, 33:18819-18831, 2020.
[54] Allen Nie, Yannis Flet-Berliac, Deon Jordan, William Steenbergen, and Emma Brunskill. Data-efficient pipeline for offline reinforcement learning with limited data. Advances in Neural Information Processing Systems, 35:14810-14823, 2022.
[55] Michael S Okun. Deep-brain stimulation for parkinson's disease. New England Journal of Medicine, 367(16):1529-1538, 2012.
[56] Michael S Okun. Deep-brain stimulation for parkinson's disease. New England Journal of Medicine, 367(16):1529-1538, 2012.
[57] Bahram Parvinian, Christopher Scully, Hanniebey Wiyor, Allison Kumar, and Sandy Weininger. Regulatory considerations for physiological closed-loop controlled medical devices used for automated critical care: food and drug administration workshop discussion topics. Anesthesia and analgesia, 126(6):1916, 2018.
[58] Vihang P Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M Blies, Johannes Brandstetter, Jose A Arjona-Medina, and Sepp Hochreiter. Align-rudder: Learning from few demonstrations by reward redistribution. arXiv preprint arXiv:2009.14108, 2020.
[59] J. Pineau, A. Guez, Robert Vincent, Gabriella Panuccio, and Massimo Avoli. Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach. Int. J. of Neural Sys., 19(04):227-240, 2009.
[60] Rob Powers, Maryam Etezadi-Amoli, Edith M Arnold, Sara Kianian, Irida Mance, Maxim Gibiansky, Dan Trietsch, Alexander Singh Alvarado, James D Kretlow, Todd M Herrington, et al. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in parkinson's disease. Science translational medicine, 13(579):eabd7865, 2021.
[61] Doina Precup. Eligibility traces for off-policy policy evaluation. Computer Science Department Faculty Publication Series, page 80, 2000.