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Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation


Nov 28, 2021
Ramtin Keramati, Omer Gottesman, Leo Anthony Celi, Finale Doshi-Velez, Emma Brunskill


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Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects


Nov 15, 2021
Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, Stefan Wager


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Play to Grade: Testing Coding Games as Classifying Markov Decision Process


Oct 27, 2021
Allen Nie, Emma Brunskill, Chris Piech

* NeurIPS 2021, 16 pages, 7 figures 

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Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making


Sep 18, 2021
Alex Chohlas-Wood, Madison Coots, Emma Brunskill, Sharad Goel


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Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning


Aug 19, 2021
Andrea Zanette, Martin J. Wainwright, Emma Brunskill

* Initial submission; appeared as spotlight talk in ICML 2021 Workshop on Theory of RL 

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On the Opportunities and Risks of Foundation Models


Aug 18, 2021
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

* Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) 

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Design of Experiments for Stochastic Contextual Linear Bandits


Jul 22, 2021
Andrea Zanette, Kefan Dong, Jonathan Lee, Emma Brunskill

* Initial submission 

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Universal Off-Policy Evaluation


Apr 26, 2021
Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, Philip S. Thomas


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Online Model Selection for Reinforcement Learning with Function Approximation


Nov 19, 2020
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill


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Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration


Aug 18, 2020
Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill


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Provably Good Batch Reinforcement Learning Without Great Exploration


Jul 22, 2020
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

* 36 pages, 7 figures 

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Learning Abstract Models for Strategic Exploration and Fast Reward Transfer


Jul 12, 2020
Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri, Kelvin Guu, Panupong Pasupat, Emma Brunskill, Percy Liang


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Power-Constrained Bandits


Apr 13, 2020
Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan Murphy, Finale Doshi-Velez


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Value Driven Representation for Human-in-the-Loop Reinforcement Learning


Apr 02, 2020
Ramtin Keramati, Emma Brunskill

* UMAP 2019, 27th ACM Conference on User Modeling, Adaptation and Personalization 

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Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding


Mar 12, 2020
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill


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Learning Near Optimal Policies with Low Inherent Bellman Error


Mar 05, 2020
Andrea Zanette, Alessandro Lazaric, Mykel Kochenderfer, Emma Brunskill

* Minor fix 

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Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions


Feb 14, 2020
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez

* Change: Correction of typo in meta-data author names 

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Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning


Jan 31, 2020
Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau


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Sublinear Optimal Policy Value Estimation in Contextual Bandits


Dec 13, 2019
Weihao Kong, Gregory Valiant, Emma Brunskill

* Extended to the mixture of Gaussians setting 

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Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare


Nov 16, 2019
Scott L. Fleming, Kuhan Jeyapragasan, Tony Duan, Daisy Ding, Saurabh Gombar, Nigam Shah, Emma Brunskill

* Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract 

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Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy


Nov 05, 2019
Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill


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Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs


Nov 03, 2019
Andrea Zanette, Emma Brunskill

* International Conference on Machine Learning, 2018 

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Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling


Oct 15, 2019
Yao Liu, Pierre-Luc Bacon, Emma Brunskill

* 21 pages, 1 figure, in submission 

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Directed Exploration for Reinforcement Learning


Jun 18, 2019
Zhaohan Daniel Guo, Emma Brunskill


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