Ray Jiang

According to our database, Ray Jiang authored at least 15 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, Oriol Vinyals

CoRR, 2023

Scaling Goal-based Exploration via Pruning Proto-goals.

Akhil Bagaria, Ray Jiang, Ramana Kumar, Tom Schaul

CoRR, 2023

Human-level Atari 200x faster.

Steven Kapturowski, Victor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Learning Expected Emphatic Traces for Deep RL.

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

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Emphatic Algorithms for Deep Reinforcement Learning.

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

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

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, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing

CoRR, 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

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

A General Approach to Fairness with Optimal Transport.

Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Wasserstein Fair Classification.

Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

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

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems.

Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan

Proceedings of the 36th International Conference on Machine Learning, 2019

Beyond Greedy Ranking: Slate Optimization via List-CVAE.

Ray Jiang, Sven Gowal, Yuqiu Qian, Timothy A. Mann, Danilo J. Rezende

Proceedings of the 7th International Conference on Learning Representations, 2019

Degenerate Feedback Loops in Recommender Systems.

Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

Learning from Delayed Outcomes with Intermediate Observations.

Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György

CoRR, 2018

Optimizing Slate Recommendations via Slate-CVAE.

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

CoRR, 2018

Delayed learning, multi-objective optimization, and whole slate generation in recommender systems.

Ray Jiang

Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems, 2018