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Learning Expected Emphatic Traces for Deep RL


Jul 12, 2021
Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt


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Emphatic Algorithms for Deep Reinforcement Learning


Jun 21, 2021
Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt

* Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021 

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Causally Correct Partial Models for Reinforcement Learning


Feb 07, 2020
Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing


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Reducing Sentiment Bias in Language Models via Counterfactual Evaluation


Nov 08, 2019
Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, Pushmeet Kohli


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Wasserstein Fair Classification


Jul 28, 2019
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

* Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019 

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Degenerate Feedback Loops in Recommender Systems


Mar 27, 2019
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli

* Proceedings of AAAI/ACM Conference on AI, Ethics, and Society, Honolulu, HI, USA, January 27-28, 2019 (AIES '19) 

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Learning from Delayed Outcomes with Intermediate Observations


Jul 24, 2018
Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, Andras Gyorgy


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Beyond Greedy Ranking: Slate Optimization via List-CVAE


May 24, 2018
Ray Jiang, Sven Gowal, Timothy A. Mann, Danilo J. Rezende

* Preliminary work. Under review by the Neural Information Processing Systems (NIPS) 2018 

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