Search Results for author: Ray Jiang

Found 11 papers, 5 papers with code

Scaling Goal-based Exploration via Pruning Proto-goals

1 code implementation9 Feb 2023 Akhil Bagaria, Ray Jiang, Ramana Kumar, Tom Schaul

One of the gnarliest challenges in reinforcement learning (RL) is exploration that scales to vast domains, where novelty-, or coverage-seeking behaviour falls short.

reinforcement-learning Reinforcement Learning (RL)

Human-level Atari 200x faster

1 code implementation15 Sep 2022 Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakićević, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia

The task of building general agents that perform well over a wide range of tasks has been an importantgoal in reinforcement learning since its inception.

Learning Expected Emphatic Traces for Deep RL

no code implementations12 Jul 2021 Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt

We develop a multi-step emphatic weighting that can be combined with replay, and a time-reversed $n$-step TD learning algorithm to learn the required emphatic weighting.

Wasserstein Fair Classification

1 code implementation28 Jul 2019 Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances.

Classification Fairness +1

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems

no code implementations24 Jul 2018 Timothy A. Mann, Sven Gowal, András György, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, Prav Srinivasan

Predicting delayed outcomes is an important problem in recommender systems (e. g., if customers will finish reading an ebook).

Recommendation Systems

Beyond Greedy Ranking: Slate Optimization via List-CVAE

1 code implementation ICLR 2019 Ray Jiang, Sven Gowal, Timothy A. Mann, Danilo J. Rezende

The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores.

Cannot find the paper you are looking for? You can Submit a new open access paper.