Delayed learning, multi-objective optimization, and whole slate generation in recommender systems. - Ray Jiang (DLRS@RecSys, 2018)

Optimizing Slate Recommendations via Slate-CVAE. - Ray Jiang; Sven Gowal; Timothy A. Mann; Danilo J. Rezende (CoRR, 2018)

Learning from Delayed Outcomes with Intermediate Observations. - Timothy A. Mann; Sven Gowal; Ray Jiang; Huiyi Hu; Balaji Lakshminarayanan; András György 001 (CoRR, 2018)

Degenerate Feedback Loops in Recommender Systems. - Ray Jiang; Silvia Chiappa; Tor Lattimore; András György 001; Pushmeet Kohli (AIES, 2019)

Beyond Greedy Ranking: Slate Optimization via List-CVAE. - Ray Jiang; Sven Gowal; Yuqiu Qian; Timothy A. Mann; Danilo J. Rezende (ICLR (Poster), 2019)

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. - Timothy A. Mann; Sven Gowal; András György 001; Huiyi Hu; Ray Jiang; Balaji Lakshminarayanan; Prav Srinivasan (ICML, 2019)

Wasserstein Fair Classification. - Ray Jiang; Aldo Pacchiano; Tom Stepleton; Heinrich Jiang; Silvia Chiappa (UAI, 2019)

A General Approach to Fairness with Optimal Transport. - Silvia Chiappa; Ray Jiang; Tom Stepleton; Aldo Pacchiano; Heinrich Jiang; John Aslanides (AAAI, 2020)

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation. - Po-Sen Huang; Huan Zhang; Ray Jiang; Robert Stanforth; Johannes Welbl; Jack Rae; Vishal Maini; Dani Yogatama; Pushmeet Kohli (EMNLP (Findings), 2020)

Causally Correct Partial Models for Reinforcement Learning. - Danilo J. Rezende; Ivo Danihelka; George Papamakarios; Nan Rosemary Ke; Ray Jiang; Theophane Weber; Karol Gregor; Hamza Merzic; Fabio Viola; Jane Wang 001; Jovana Mitrovic; Frederic Besse; et al. (CoRR, 2020)

Emphatic Algorithms for Deep Reinforcement Learning. - Ray Jiang; Tom Zahavy; Zhongwen Xu; Adam White 001; Matteo Hessel; Charles Blundell; Hado van Hasselt (ICML, 2021)

Learning Expected Emphatic Traces for Deep RL. - Ray Jiang; Shangtong Zhang; Veronica Chelu; Adam White 001; Hado van Hasselt (AAAI, 2022)

Human-level Atari 200x faster. - Steven Kapturowski; Victor Campos 001; Ray Jiang; Nemanja Rakicevic; Hado van Hasselt; Charles Blundell; Adrià Puigdomènech Badia (ICLR, 2023)

Scaling Goal-based Exploration via Pruning Proto-goals. - Akhil Bagaria; Ray Jiang; Ramana Kumar; Tom Schaul (CoRR, 2023)

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning. - Michaël Mathieu; Sherjil Ozair; Srivatsan Srinivasan; Çaglar Gülçehre; Shangtong Zhang; Ray Jiang; Tom Le Paine; Richard Powell; Konrad Zolna; Julian Schrittwieser; David H. Choi; Petko Georgiev; et al. (CoRR, 2023)